Methods and systems for classifying fluorescent flow cytometer data

ABSTRACT

Methods for classifying fluorescent flow cytometer data are provided. In some instances, methods include processing the flow cytometer data with a supervised algorithm configured to cluster the fluorescent flow cytometer data into distinct populations according to the relationship of data points to relevant threshold values. In embodiments, methods include determining a measure of spillover spreading by calculating spillover spreading coefficients and combining them in a spillover spreading matrix. In some embodiments, populations of fluorescent flow cytometer data are adjusted to subtract the magnitude of spillover spreading. In embodiments, spillover spreading adjusted populations are partitioned after potential partitions are evaluated relative to the threshold values. In embodiments, partitioned populations of fluorescent flow cytometer data are classified (i.e., phenotyped) according to a hierarchy. Systems and computer-readable media for classifying fluorescent flow cytometer data are also provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

Pursuant to 35 U.S.C. § 119 (e), this application claims priority to thefiling date of U.S. Provisional Patent Application Ser. No. 62/968,516,filed Jan. 31, 2019, and to the filing date of U.S. Provisional PatentApplication Ser. No. 63/053,108 filed Jul. 17, 2019, the disclosures ofwhich applications are incorporated herein by reference.

INTRODUCTION

Flow cytometry is a technique used to characterize and often times sortbiological material, such as cells of a blood sample or particles ofinterest in another type of biological or chemical sample. A flowcytometer typically includes a sample reservoir for receiving a fluidsample, such as a blood sample, and a sheath reservoir containing asheath fluid. The flow cytometer transports the particles (includingcells) in the fluid sample as a cell stream to a flow cell, while alsodirecting the sheath fluid to the flow cell. To characterize thecomponents of the flow stream, the flow stream is irradiated with light.Variations in the materials in the flow stream, such as morphologies orthe presence of fluorescent labels, may cause variations in the observedlight and these variations allow for characterization and separation.For example, particles, such as molecules, analyte-bound beads, orindividual cells, in a fluid suspension are passed by a detection regionin which the particles are exposed to an excitation light, typicallyfrom one or more lasers, and the light scattering and fluorescenceproperties of the particles are measured. Particles or componentsthereof typically are labeled with fluorescent dyes to facilitatedetection. A multiplicity of different particles or components may besimultaneously detected by using spectrally distinct fluorescent dyes tolabel the different particles or components. In some implementations, amultiplicity of detectors, one for each of the scatter parameters to bemeasured, and one or more for each of the distinct dyes to be detectedare included in the analyzer. For example, some embodiments includespectral configurations where more than one sensor or detector is usedper dye. The data obtained comprise the signals measured for each of thelight scatter detectors and the fluorescence emissions.

Flow cytometers may further comprise means for recording the measureddata and analyzing the data. For example, data storage and analysis maybe carried out using a computer connected to the detection electronics.For example, the data can be stored in tabular form, where each rowcorresponds to data for one particle, and the columns correspond to eachof the measured features. The use of standard file formats, such as an“FCS” file format, for storing data from a particle analyzer facilitatesanalyzing data using separate programs and/or machines. Using currentanalysis methods, the data typically are displayed in 1-dimensionalhistograms or 2-dimensional (2D) plots for ease of visualization, butother methods may be used to visualize multidimensional data.

The parameters measured using, for example, a flow cytometer typicallyinclude light at the excitation wavelength scattered by the particle ina narrow angle along a mostly forward direction, referred to as forwardscatter (FSC), the excitation light that is scattered by the particle inan orthogonal direction to the excitation laser, referred to as sidescatter (SSC), and the light emitted from fluorescent molecules in oneor more detectors that measure signal over a range of spectralwavelengths, or by the fluorescent dye that is primarily detected inthat specific detector or array of detectors. Different cell types canbe identified by their light scatter characteristics and fluorescenceemissions resulting from labeling various cell proteins or otherconstituents with fluorescent dye-labeled antibodies or otherfluorescent probes.

Both flow and scanning cytometers are commercially available from, forexample, BD Biosciences (San Jose, Calif.). Flow cytometry is describedin, for example, Landy et al. (eds.), Clinical Flow Cytometry, Annals ofthe New York Academy of Sciences Volume 677 (1993); Bauer et al. (eds.),Clinical Flow Cytometry: Principles and Applications, Williams & Wilkins(1993); Ormerod (ed.), Flow Cytometry: A Practical Approach, OxfordUniv. Press (1994); Jaroszeski et al. (eds.), Flow Cytometry Protocols,Methods in Molecular Biology No. 91, Humana Press (1997); and PracticalShapiro, Flow Cytometry, 4th ed., Wiley-Liss (2003); all incorporatedherein by reference. Fluorescence imaging microscopy is described in,for example, Pawley (ed.), Handbook of Biological Confocal Microscopy,2nd Edition, Plenum Press (1989), incorporated herein by reference.

After flow cytometer data is received from one or more detectors, it isoften subjected to a data analysis process through which it can be madeintelligible to the user. In some cases, flow cytometer data analysisinvolves determining the phenotype associated with the analytes (e.g.,cells, particles) being irradiated in the flow cytometer. For themajority of all cytometry samples, it is necessary to identify cell typesubsets (i.e., phenotypes) before any deeper analysis is possible. In atypical workflow, a pre-planned gating hierarchy recapitulatinguniversally accepted definitions of cell types must be manually adjustedto suit the data of each sample (to account for biological effects,batch effects, and any other peculiarities). Adjustment approaches canbe either “unsupervised” or “supervised”. Unsupervised approaches beginwith clustering flow cytometer data into populations, and thenattempting to assign cell type labels (i.e., phenotypes) to eachpopulation cluster using some a priori knowledge regarding theassociation of particular analytes with certain parameters (e.g.,fluorescence). On the other hand, supervised approaches typically beginwith establishing a gating hierarchy, i.e., a set of rules governing howan individual cell is classified (i.e., phenotyped) based on its statuswith respect to one or more parameters. Subsequently, flow cytometerdata is “fit” to the gating hierarchy such that individual data pointsfall somewhere on the hierarchy. The DAFi algorithm, for example, fitsclusters of events to an existing gating tree based on their centroids,which allows cell types to be delineated along natural boundaries in thedata rather than fixed gate boundaries. The DAFi algorithm is describedin, for example, Lee et al. (2018). DAR: a directed recursive datafiltering and clustering approach for improving and interpreting dataclustering identification of cell populations from polychromatic flowcytometry data. Cytometry Part A, 93(6), 597-610: herein incorporated byreference. FlowDensity, on the other hand, finds density troughs in thedata to use as gate boundaries for a pre-defined hierarchy. TheFlowDensity Algorithm is described in, for example, Malek et al. (2015).flowDensity: reproducing manual gating of flow cytometry data byautomated density-based cell population identification. Bioinformatics,31(4), 606-607; herein incorporated by reference. However, the DAFialgorithm can only account for small sample-to-sample variation, and theFlowDensity algorithm requires hands-on auto-phenotyping expert tuningon a per-panel basis.

The Ek′ Balam algorithm represents an approach to flow cytometer dataadjustment that improves upon the DAFi and FlowDensity algorithms inthat the Ek′ Balam algorithm is easy to tune and can handle largesample-to-sample variation. However, flow cytometry data analysisprotocols involving the measurement of two or more parameters (e.g.,fluorescent signals), such as the Ek′ Balam algorithm, are complicatedby spillover, a phenomenon in which particle-modulated light indicativeof a particular fluorochrome is received by one or more detectors thatare not configured to measure that parameter. As such, light may“spill-over” and be detected by off-target detectors. Spillover can becorrected by unmixing, in which new per-fluorochrome intensity valuesare calculated by solving a system of equations relating thefluorochrome intensity values to the measured detector values via theobserved levels of spillover. Unmixing is often called “compensation”when the number of detectors is equal to the number of fluorochromesbeing unmixed. Although unmixing corrects intensity contributions fromeach fluorochrome into each other fluorochrome, it cannot correct noisecontributions, i.e., the error contributed to the fluorescent flowcytometer data by spillover. This noise is called “spillover spreading”.In some instances, spillover spreading noise is constructive, whichresults in signal intensities that are higher than would otherwise beobserved, while in other instances the noise is destructive, resultingin lower intensities. For example, FIG. 1 demonstrates how population101 spreads due to presence of noise from fluorochrome A on detectorsused to measure fluorochrome B. Due to population spread, classification(i.e., phenotyping) of fluorescent flow cytometer data by data analysisprotocols may be rendered inaccurate. For example, FIG. 2A and FIG. 2Bdemonstrate the effect of spillover spreading on the Ek′ Balamalgorithm's ability to correctly distinguish between distinct populationclusters of flow cytometer data. As shown in FIG. 2B, population 101 ispartitioned incorrectly and a portion of the population is consequentlyassigned an incorrect phenotype (i.e., A⁺B⁺ rather than A⁺B⁻).Accordingly, a solution for spillover spreading in flow cytometer dataanalysis is required.

SUMMARY

Aspects of the invention include methods for classifying fluorescentflow cytometer data. In some embodiments, the methods include processingthe flow cytometer data with a supervised algorithm configured tocluster the fluorescent flow cytometer data into populations. Inembodiments, fluorescent flow cytometer data is clustered based on eachdata point's status relative to a hierarchy. In such embodiments,fluorescent flow cytometer data is clustered into populations based onpositivity or negativity of the fluorescent flow cytometer data withrespect to particular fluorochromes. In some embodiments, fluorescentflow cytometer data is determined to be positive or negative for aparticular fluorochrome based on a relationship of the fluorescent flowcytometer data to a threshold value. After data is clustered,embodiments of the instant method include determining a measure ofspillover spreading. In some embodiments, determining spilloverspreading includes calculating a spillover spreading coefficient thatquantifies the extent to which the intensity of light collected from afirst fluorochrome by a first detector is impacted by the simultaneouscollection of light from a second fluorochrome by the same detector. Inembodiments, a spillover spreading coefficient is calculated for eachpossible combination of fluorescent light detector and fluorochrome inorder to determine the extent to which light emitting from a particularfluorochrome is collected at a given detector. In some embodiments,spillover spreading coefficients are combined in a spillover spreadingmatrix. In certain embodiments, populations of fluorescent flowcytometer data are adjusted to account for the magnitude of spilloverspreading determined in the spillover spreading matrix. Practicing theinstant method may further include partitioning the distinct spilloverspreading adjusted populations of fluorescent flow cytometer data. Inembodiments, partitioning the populations involves calculating Matthew'scorrelation coefficient, and establishing a partition delineatingbetween separate populations (i.e., populations exhibiting differentfluorescent parameters) in a manner that optimizes Matthew's correlationcoefficient with respect to each relevant threshold. In embodiments,partitioned populations of fluorescent flow cytometer data aresubsequently classified such that the distinct populations areassociated with a respective subtype (i.e., phenotype).

Aspects of the invention also include systems for classifyingfluorescent flow cytometer data. In some embodiments, systems include aparticle analyzer configured to produce fluorescent flow cytometer data.Systems may also include a processor having memory operably coupled tothe processor wherein the memory includes instructions stored thereon,which when executed by the processor, cause the processor to process theflow cytometer data with a supervised algorithm configured to clusterthe fluorescent flow cytometer data into populations. In embodiments,fluorescent flow cytometer data is clustered based on each data point'sstatus relative to a hierarchy. In such embodiments, fluorescent flowcytometer data is clustered into populations based on positivity ornegativity of the fluorescent flow cytometer data with respect toparticular fluorochromes. In some embodiments, fluorescent flowcytometer data is determined to be positive or negative for a particularfluorochrome based on a relationship of the fluorescent flow cytometerdata to a threshold value. After data is clustered, the processor may beconfigured to determine a measure of spillover spreading. In someembodiments, determining spillover spreading includes calculating aspillover spreading coefficient that quantifies the extent to which theintensity of light collected from a first fluorochrome by a firstdetector is impacted by the simultaneous collection of light from asecond fluorochrome by the same detector. In embodiments, a spilloverspreading coefficient is calculated for each possible combination offluorescent light detector and fluorochrome in order to determine theextent to which light emitting from a particular fluorochrome iscollected at a given detector. In some embodiments, the processor isconfigured to combine spillover spreading coefficients in a spilloverspreading matrix. In certain embodiments, populations of fluorescentflow cytometer data are adjusted to account for the magnitude ofspillover spreading determined in the spillover spreading matrix. Theprocessor may be further configured to partition the distinct spilloverspreading adjusted populations of fluorescent flow cytometer data. Inembodiments, partitioning the populations involves calculating Matthew'scorrelation coefficient, and establishing a partition delineatingbetween separate populations (i.e., populations exhibiting differentfluorescent parameters) in a manner that optimizes Matthew's correlationcoefficient with respect to each relevant threshold. In embodiments,partitioned populations of fluorescent flow cytometer data aresubsequently classified such that the distinct populations areassociated with a respective subtype (i.e., phenotype).

Aspects of the present disclosure further include computer-controlledsystems, where the systems further include one or more computers forcomplete automation or partial automation. In some embodiments, systemsinclude a computer having a computer readable storage medium with acomputer program stored thereon, where the computer program when loadedon the computer includes instructions for clustering fluorescent flowcytometer data into populations according to one or more differentparameters (i.e., fluorochromes), determining the spillover spreadingbetween each detector-parameter pair (i.e., by calculating spilloverspreading coefficients), creating a spillover spreading matrixdemonstrating how the detection of a particular fluorochrome by itscorresponding detector is impacted by spillover from otherfluorochromes, adjusting the fluorescent flow cytometer data tocompensate for spillover spreading by subtracting the magnitude of thespillover spreading as determined by the spillover spreading matrix,evaluating the quality of different partitions separating distinctpopulations of fluorescent flow cytometer data by calculating Matthew'scorrelation coefficient with respect to thresholds distinguishingbetween populations that are positive for a given parameter andpopulation that are negative for a given parameter, and classifying(i.e., phenotyping) adjusted populations of fluorescent flow cytometerdata.

BRIEF DESCRIPTION OF THE FIGURES

The invention may be best understood from the following detaileddescription when read in conjunction with the accompanying drawings.Included in the drawings are the following figures:

FIG. 1 depicts spillover spreading in fluorescent flow cytometer data.

FIG. 2A depicts how spillover spreading inhibits an algorithm's abilityto correctly distinguish between distinct population clusters offluorescent flow cytometer data.

FIG. 2B depicts how spillover spreading inhibits an algorithm's abilityto classify (i.e., phenotype) populations of fluorescent flow cytometerdata.

FIG. 3 depicts a hierarchy for phenotyping T cells by determining thepositivity or negativity of cells with respect to the presence CD4 orCD8.

FIG. 4 depicts clustering fluorescent flow cytometer data relative tothreshold values.

FIG. 5 depicts a spillover spreading matrix.

FIG. 6 depicts the process of adjusting a population of flow cytometerdata based on spillover spreading.

FIG. 7A depicts flow cytometer data that has been adjusted based onspillover spreading.

FIG. 7B depicts classifying (i.e., phenotyping) flow cytometer data thathas been adjusted based on spillover spreading.

FIG. 8 depicts a flow cytometer according to certain embodiments.

FIG. 9 depicts a functional block diagram for one example of a processoraccording to certain embodiments.

FIG. 10 depicts a block diagram of a computing system according tocertain embodiments.

DETAILED DESCRIPTION

Methods for classifying fluorescent flow cytometer data are provided. Insome instances, methods include processing the flow cytometer data witha supervised algorithm configured to cluster the fluorescent flowcytometer data into distinct populations according to the relationshipof data points to relevant threshold values. In embodiments, methodsinclude determining a measure of spillover spreading by calculatingspillover spreading coefficients and combining them in a spilloverspreading matrix. In some embodiments, populations of fluorescent flowcytometer data are adjusted to subtract the magnitude of spilloverspreading as determined by the spillover spreading matrix. Inembodiments, spillover spreading adjusted populations are partitionedafter potential partitions are evaluated relative to the thresholdvalues. In embodiments, partitioned populations of fluorescent flowcytometer data are classified (i.e., phenotyped) according to ahierarchy. Systems and computer-readable media for classifyingfluorescent flow cytometer data are also provided.

Before the present invention is described in greater detail, it is to beunderstood that this invention is not limited to particular embodimentsdescribed, as such may, of course, vary. It is also to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only, and is not intended to be limiting, sincethe scope of the present invention will be limited only by the appendedclaims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range, is encompassed within the invention. The upper and lowerlimits of these smaller ranges may independently be included in thesmaller ranges and are also encompassed within the invention, subject toany specifically excluded limit in the stated range. Where the statedrange includes one or both of the limits, ranges excluding either orboth of those included limits are also included in the invention.Certain ranges are presented herein with numerical values being precededby the term “about.” The term “about” is used herein to provide literalsupport for the exact number that it precedes, as well as a number thatis near to or approximately the number that the term precedes. Indetermining whether a number is near to or approximately a specificallyrecited number, the near or approximating unrecited number may be anumber which, in the context in which it is presented, provides thesubstantial equivalent of the specifically recited number.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present invention, representativeillustrative methods and materials are now described.

All publications and patents cited in this specification are hereinincorporated by reference as if each individual publication or patentwere specifically and individually indicated to be incorporated byreference and are incorporated herein by reference to disclose anddescribe the methods and/or materials in connection with which thepublications are cited. The citation of any publication is for itsdisclosure prior to the filing date and should not be construed as anadmission that the present invention is not entitled to antedate suchpublication by virtue of prior invention. Further, the dates ofpublication provided may be different from the actual publication dateswhich may need to be independently confirmed.

It is noted that, as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise. It is further noted that the claimsmay be drafted to exclude any optional element. As such, this statementis intended to serve as antecedent basis for use of such exclusiveterminology as “solely,” “only” and the like in connection with therecitation of claim elements, or use of a “negative” limitation.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentinvention. Any recited method can be carried out in the order of eventsrecited or in any other order which is logically possible.

While the system and method has or will be described for the sake ofgrammatical fluidity with functional explanations, it is to be expresslyunderstood that the claims, unless expressly formulated under 35 U.S.C.§ 112, are not to be construed as necessarily limited in any way by theconstruction of “means” or “steps” limitations, but are to be accordedthe full scope of the meaning and equivalents of the definition providedby the claims under the judicial doctrine of equivalents, and in thecase where the claims are expressly formulated under 35 U.S.C. § 112 areto be accorded full statutory equivalents under 35 U.S.C. § 112.

Methods for Classifying Fluorescent Flow Cytometer Data

As discussed above, aspects of the present disclosure include methodsfor classifying fluorescent flow cytometer data. By “fluorescent flowcytometer data” it is meant information regarding parameters of a sample(e.g., cells, particles) in a flow cell that is collected by any numberof fluorescent light detectors in a particle analyzer. In embodiments,fluorescent flow cytometer data includes signals from a plurality ofdifferent fluorochromes, such as, for instance, ranging from 2 to 20different fluorochromes, and including 3 to 5 different fluorochromes.In some embodiments, a plurality of different fluorochromes includes 2or more different fluorochromes, including 3 or more, 4 or more, 5 ormore, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11, ormore, 12 or more, 13 or more, 14 or more 15 or more, and 20 or moredifferent fluorochromes. Fluorescent flow cytometer data may be obtainedby any convenient protocol, including those described below.

In some embodiments, methods include generating one or more populationclusters based on the determined parameters (e.g., fluorescence) ofanalytes (e.g., cells, particles) in the sample. As used herein, a“population”, or “subpopulation” of analytes, such as cells or otherparticles, generally refers to a group of analytes that possessproperties (for example, optical, impedance, or temporal properties)with respect to one or more measured fluorescent parameters such thatmeasured parameter data form a cluster in the data space. Thus,populations are recognized as clusters in the data. Conversely, eachdata cluster generally is interpreted as corresponding to a populationof a particular type of cell or analyte, although clusters thatcorrespond to noise or background typically also are observed. A clustermay be defined in a subset of the dimensions, e.g., with respect to asubset of the measured fluorescent parameters (i.e., fluorochromes),which corresponds to populations that differ in only a subset of themeasured parameters or features extracted from the measurements of thesample.

Aspects of the invention include the supervised clustering offluorescent flow cytometer data. Any convenient supervised phenotypingalgorithm may be employed for this step. In some embodiments, thesupervised phenotyping algorithm is the Ek′ Balam algorithm. The Ek′Balam algorithm is described in Amir et al. (2019). Development of acomprehensive antibody staining database using a standardized analyticspipeline. Frontiers in immunology, 10, 1315.; herein incorporated byreference. As such, in certain embodiments, populations of fluorescentflow cytometer data are clustered based on each data point's statusrelative to a hierarchy. A hierarchy as described herein defines thecriteria by which fluorescent flow cytometer data is grouped into aparticular population. In some embodiments, the hierarchy establisheshow data points that are positive or negative for the same parametersare grouped together. For example, FIG. 3 demonstrates a hierarchy forclustering T cells by determining the positivity or negativity of thecells with respect to the presence of CD4 and CD8. A cell that ispositive for CD4 but negative for CD8 is a “CD4 T Cell”, while a cellthat is positive for both markers is a “Double Positive T Cell”, and soforth.

In some embodiments, clustering fluorescent flow cytometer data includescomparing the fluorescent flow cytometer data to a threshold. By“threshold” it is meant a value that distinguishes betweenpositive/negative fluorescent flow cytometer data with respect to aparticular fluorochrome. In other words, fluorescent flow cytometer datathat lies above the threshold value may be described as positive for aparticular fluorochrome, while fluorescent flow cytometer data that liesbelow the threshold value may be described as negative for the samefluorochrome. For example, FIG. 4 depicts how flow cytometer data isclustered according to threshold values distinguishing between positiveand negative data. Threshold 401 distinguishes flow cytometer data thatis positive for CD8 from flow cytometer that is negative for CD8.Similarly, threshold 402 distinguishes flow cytometer data that ispositive for CD4 from flow cytometer that is negative for CD4.Clustering may be performed by any convenient method. In someembodiments, clustering is performed by the FlowSOM algorithm. FlowSOMis described in Van Lassen et al. (2015). FlowSOM: Using self-organizingmaps for visualization and interpretation of cytometry data. CytometryPart A, 87(7), 636-645; herein incorporated by reference. In otherembodiments, clustering is performed by an algorithm selected from:SOM—described in Kohonen, “The self-organizing map,” Proceedings of theIEEE (1990) 78: 1464-1480; K-means—described in Hartigan & Wong,“Algorithm AS 136: A K-Means Clustering Algorithm,” Journal of the RoyalStatistical Society. Series C (Applied Statistics) (1979) 28: pp.100-108; Gaussian Mixture Models—described in Sanderson & Curtin, “Anopen source C++ implementation of multi-threaded Gaussian mixturemodels, k-means and expectation maximisation,” 2017 11th InternationalConference on Signal Processing and Communication Systems (ICSPCS)(https://doi.org/10.1109/ICSPCS.2017.8270510); FlowGrid—as described inYe & Ho, “Ultrafast clustering of single-cell flow cytometry data usingFlowGrid,” BMC Syst Biol 13, 35 (2019).https://doi.org/10.1186/s12918-019-0690-2; or X-shift—described inSamusik et al., “Automated mapping of phenotype space with single-celldata,” Nat Methods (2016) 13: 493-496.

After flow cytometer data is clustered, embodiments of the inventioninclude determining a measure of spillover spreading for the populationsof fluorescent flow cytometer data. As described in the Introductionsection and depicted in FIG. 1 and FIG. 2A-2B, fluorescent flowcytometer data at the point of collection (i.e., the point at which itis received by one or more fluorescent light detectors) is subject tospillover spreading. Spillover is a phenomenon in whichparticle-modulated light indicative of a particular fluorochrome isreceived by one or more detectors that are not configured to measurethat parameter. As such, light may “spill-over” and be detected byoff-target detectors. Spillover spreading, therefore, is noise presentin the fluorescent flow cytometer data caused by spillover. As such, insome embodiments, unadjusted flow cytometer data is erroneous due to theunintentional detection of certain wavelengths of light by one or moredetectors. In certain embodiments, determining a measure of spilloverspreading includes quantifying the extent to which fluorescent flowcytometer data collected from a first fluorochrome by a first detectoris impacted by the simultaneous collection of light from a secondfluorochrome by the same detector. In some instances, fluorescent flowcytometer data subject to spillover spreading is impacted by signalintensities that are higher than would otherwise be observed (i.e., thespillover spreading noise is constructive). In other instances,fluorescent flow cytometer data subject to spillover spreading isimpacted by signal intensities that are lower than otherwise would beobserved (i.e., the spillover spreading noise is destructive). Incertain embodiments, determining a measure of spillover spreadingincludes calculating a spillover spreading coefficient. Spilloverspreading coefficients are described in detail in Nguyen et al. (2013).Quantifying spillover spreading for comparing instrument performance andaiding in multicolor panel design. Cytometry Part A, 83(3), 306-315.;the disclosure of which is incorporated by reference herein. In someembodiments, a spillover spreading coefficient is calculated accordingto Equation 1:

${S\; S} = {\frac{\Delta\sigma_{f}}{\sqrt{\Delta d}} = \frac{\sqrt{\left( \sigma_{pos} \right)^{2} - \left( \sigma_{neg} \right)^{2}}}{\sqrt{d_{pos} - d_{neg}}}}$

As shown in Equation 1, SS is the spillover spreading coefficient,Δσ_(f) is an incremental standard deviation indicating the spread of theemission between the positive and negative fluorescent flow cytometerdata collected from a fluorochrome, and Δd is a difference in theintensity of the fluorescent light between the positive and negativefluorescent flow cytometer data received by a fluorescent lightdetector. As such, the spillover spreading coefficient measures theextent to which fluorescent flow cytometer data collected by a givenfluorescent light detector is impacted by the presence of lightassociated with a particular fluorochrome. In other words, the spilloverspreading coefficient estimates the error (i.e., noise) contributed tothe fluorescent flow cytometer data by light emitting from the relevantfluorochrome being collected by a given detector. In embodiments, ahigher spillover spreading coefficient corresponds to more spilloverspreading for a given fluorochrome-detector pair.

In some embodiments, determining a measure of spillover spreading alsoincludes calculating spillover spreading coefficients for each possiblefluorescent light detector-fluorochrome combination so that it can bedetermined how fluorescent flow cytometer data collected at eachdetector is affected by the presence of light associated with eachfluorochrome. In embodiments, spillover spreading coefficientscalculated for each fluorescent light detector-fluorochrome pair arecombined in a spillover spreading matrix. In certain embodiments, thespillover spreading matrix demonstrates how the detection of aparticular fluorochrome by its corresponding detector is impacted byspillover from other fluorochromes. For example, FIG. 5 presents oneembodiment of a spillover spreading matrix that provides spilloverspreading coefficients for 23 different fluorochromes. Each column inthe matrix corresponds to a detector configured to detect one of the 23different fluorochromes, and each row in the matrix corresponds to aparameter of flow cytometer data that is detected. The cell in which acolumn and row intersects is populated with a spillover spreadingcoefficient calculated for that fluorescent light detector-fluorochromepair indicating the extent to which the fluorochrome in questioncontributes error to the relevant detector. The total degree to which afluorochrome causes spillover spreading can be approximated by summingall the values in its row, and the total degree to which a detector isimpacted by spillover spreading can be calculated by summing all thevalues in its column. In some embodiments, spillover spreadingcoefficients are summed in order to calculate the total spreading effect(i.e., the cumulative effect of spillover spreading on a particularsubset of fluorescent flow cytometer data).

In some embodiments, the spillover spreading matrix is calculated bymeans of the AutoSpread algorithm. The AutoSpread algorithm was createdby Becton Dickinson and is described U.S. Provisional Application No.63/020,758, herein incorporated by reference, and is configured tocreate a spillover spreading matrix (e.g., as described above) withoutrequiring a distinction between populations of flow cytometer that arepositive and negative with respect to a given fluorochrome. AutoSpreadcharacterizes the spread contributed to the detected signal of a firstfluorochrome by the inclusion of a second fluorochrome in the same flowcytometry panel. AutoSpread produces one coefficient for eachinteraction between a fluorescent light detector and a fluorochrome, andarranges the coefficients into a matrix akin to the spillover spreadingmatrix described above. In embodiments, calculating a spilloverspreading coefficient includes assuming that the intensity offluorescent light collected by the fluorescent light detector for thenegative population of flow cytometer data is zero, and thecorresponding standard deviation is an unknown quantity. In someembodiments, the spillover spreading coefficient is calculated accordingto Equation 2:

${S\; S} = \frac{\sqrt{\sigma^{2} - \sigma_{0}^{2}}}{\sqrt{d}}$

As shown in Equation 2, SS is the spillover spreading coefficient, σ² isthe standard deviation of the positive population of fluorescent flowcytometer data, σ₀ ² is an estimate of the standard deviation of thenegative population of fluorescent flow cytometer data, and d is theintensity of light collected by a fluorescent light detector. In someembodiments, in order to obtain an estimate of the standard deviation ofthe negative population of fluorescent flow cytometer data (σ₀ ²) whenthe intensity of fluorescent light collected by the fluorescent lightdetector for the negative population of flow cytometer data is assumedto be zero, the spillover spreading coefficient is calculated followinga sequence of linear regressions. Fluorescent flow cytometer data isfirst sorted into quantiles according to intensity values that aredetected by the fluorescent light detector. The number of quantiles isby default 256, but is adjusted downwards to as few as 8 to ensure thateach quantile has a sufficient number of data points to allow forreliable estimation of standard deviations. Next, the robust standarddeviation of the light emitted from the fluorochrome is regressedagainst the square root of the median intensity of light detected foreach quantile. The y-intercept of the ordinary least squares fit istaken as the estimate of the standard deviation in the negativepopulation of flow cytometer data when the intensity of light detectedfor the negative population is assumed to be zero. The estimate of thestandard deviation of light emitted from the fluorochrome is used toobtain new zero-adjusted standard deviations. The zero-adjusted standarddeviation for the fluorochrome is regressed against the square root ofthe median fluorescence of each quantile detected by the fluorescentlight detector. The slope of the ordinary least squares fit (calculatedby Equation 2) is taken as the spillover spreading coefficient.

Aspects of the present disclosure further include adjusting fluorescentflow cytometer data to account for spillover spreading. By “adjusting”it is meant altering the data such that it more accurately quantifiesthe presence of fluorochromes in the sample (e.g., cells, particles)being irradiated in the flow cell. In some embodiments, fluorescent flowcytometer data is adjusted to remove substantially all constructiveerror resulting from spillover spreading. In embodiments, adjustingfluorescent flow cytometer data includes generating distinct spilloverspreading adjusted populations. In certain embodiments, generatingdistinct spillover spreading adjusted populations includes subtractingthe magnitude of the spillover spreading from the relevant population(s)of flow cytometer data, i.e., to counteract the constructive effects ofsignals being impacted by spillover spreading. In certain embodiments,the magnitude of spillover spreading is determined from the spilloverspreading matrix. In some embodiments, adjusting flow cytometer dataincludes subtracting the total spreading effect from the relevantportion of the flow cytometer data. For example, FIG. 6 demonstrates theadjustment of flow cytometer data such that spillover spreading isaccounted for. Arrows 601 indicates how the median of the populationcluster is being shifted downwards. In other words, the magnitude ofspillover spreading is being subtracted from the datapoints within thepopulation cluster, thereby altering the position of the clusterrelative to other population clusters. FIG. 7A depicts flow cytometerdata that has been adjusted by the above-described process. In someembodiments, fluorescent flow cytometer data is adjusted by calculatingthe likelihood that each cell falls above a threshold according to allknown sources of noise, including spillover spreading.

After populations of fluorescent flow cytometer data have been adjustedfor spillover spreading (e.g., as described above), aspects of thepresent invention further include partitioning the spillover spreadingadjusted populations of fluorescent flow cytometer data. By“partitioning” it is meant delineating between populations of flowcytometer data that are determined to be separate based on theirpositivity or negativity with respect to the plurality of differentfluorochromes. In other words, partitions are created to formallydistinguish between populations of fluorescent flow cytometer data thatare to be classified differently (e.g., represent different phenotypes).

In embodiments, partitioning distinct spillover spreading adjustedpopulations of flow cytometer data includes calculating Matthew'scorrelation coefficient (MCC). This coefficient is a measure of qualityfor binary classifications, and provides a quantification of how well agiven threshold accurately distinguishes between positive and negativegroups of flow cytometer data. Matthew's correlation coefficient isdescribed in Matthews, B. W. (1975). Comparison of the predicted andobserved secondary structure of T4 phage lysozyme. Biochimica etBiophysica Acta (BBA)-Protein Structure, 405(2), 442-451.; thedisclosure of which is incorporated by reference. In embodiments,potential partitions between distinct spillover spreading adjustedpopulations of fluorescent flow cytometer data are evaluated withrespect to each of the threshold values. In such embodiments, Matthew'scorrelation coefficient is calculated for each of the threshold values,thereby assessing the level of agreement between the threshold and thepartition. Embodiments of the invention therefore involve partitioningthe populations such that Matthew's correlation coefficient is optimizedwith respect to the relevant thresholds distinguishing between positiveand negative populations of flow cytometer data with respect to aparticular fluorochrome. In other words, partitioning fluorescent flowcytometer data includes maximizing the extent to which separatepopulations (i.e., populations that exhibit different combinations offluorescent parameters) are distinguished from each other as determinedby their relationship to relevant thresholds (e.g., quantified byMatthew's correlation coefficient). In embodiments, this process isiterated to determine optimal partitions distinguishing between positiveand negative populations for each relevant parameter of flow cytometerdata. In embodiments, Matthew's correlation coefficient is calculatedaccording to Equation 3:

${M\; C\; C} = \frac{{TP \times TN} - {FP \times FN}}{\sqrt{\left( {{TP} + {FP}} \right)\left( {{TP} + {FN}} \right)\left( {{TN} + {FP}} \right)\left( {{TN} + {FN}} \right)}}$

As shown in Equation 3, MCC is Matthew's correlation coefficient, TPrepresents true positive events, TN represents true negative events, FPrepresents false positive events and FN represents false negativeevents. According to the present disclosure, true positive events arefluorescent flow cytometer data that are assessed to be positive for aparticular fluorochrome by both the threshold and the partition, truenegative events are fluorescent flow cytometer data that are assessed tobe negative for a particular fluorochrome by both the threshold and thepartition, false positive events are fluorescent flow cytometer datathat are assessed to be positive for a particular fluorochrome by thepartition and negative by the threshold, and false negative events areflow cytometer data that are assessed to be negative for a particularfluorochrome by the partition and positive by the threshold.

In some embodiments of the invention, fluorescent flow cytometer datadoes not contain signals that are positive for a particularfluorochrome. In other words, fluorescent light emitting from thefluorochrome is not detected. In such embodiments, partitioning thedistinct spillover spreading adjusted fluorescent flow cytometer datamay comprise calculating the balanced accuracy of each partition as analternative metric for determining the optimal partition. Balancedaccuracy is the average of the accuracy of determining positive eventsand the accuracy of determining negative events and is calculatedaccording to Equation 4:

${B\; A} = \frac{\left( \frac{TP}{{TP} + {FN}} \right) + \left( \frac{TN}{{TN} + {FP}} \right)}{2}$

As shown in Equation 4, BA is balanced accuracy, TP represents truepositive events, TN represents true negative events, FP represents falsepositive events and FN represents false negative events.

Embodiments of the invention also include classifying the partitionedpopulations of fluorescent flow cytometer data, i.e., determining thesubtype of cells or particles designated by each distinct spilloverspreading adjusted population of flow cytometer data. In embodiments,classifications are determined based on the hierarchy (described above).As such, a population of fluorescent flow cytometer data that has beenpartitioned (e.g., as described above) is assigned a classification(i.e., phenotyped) based on the combination of fluorescent parameters itexhibits. For example, FIG. 7B depicts how the spillover spreadingadjusted fluorescent flow cytometer data depicted in FIG. 7A ispartitioned such that distinct population clusters are correctlyassigned to their respective phenotypes. As shown in FIG. 2B, discussedin the Introduction section, a portion of a population cluster 101 isassigned the incorrect phenotype absent any data adjustment. However,after the population is adjusted (arrows 601) for spillover spreading(e.g., as shown in FIG. 6), the entirety of population 701 is assignedthe correct phenotype (i.e., A⁺B⁻), as opposed to only part ofpopulation 701 being assigned the correct phenotype.

As summarized above, the fluorescent data employed in methods of theinvention may be obtained using any convenient protocol. In someembodiments, a sample having particles is irradiated with a light sourceand light from the sample is detected to generate populations of relatedparticles based at least in part on the measurements of the detectedlight. In some instances, the sample is a biological sample. The term“biological sample” is used in its conventional sense to refer to awhole organism, plant, fungi or a subset of animal tissues, cells orcomponent parts which may in certain instances be found in blood, mucus,lymphatic fluid, synovial fluid, cerebrospinal fluid, saliva,bronchoalveolar lavage, amniotic fluid, amniotic cord blood, urine,vaginal fluid and semen. As such, a “biological sample” refers to boththe native organism or a subset of its tissues as well as to ahomogenate, lysate or extract prepared from the organism or a subset ofits tissues, including but not limited to, for example, plasma, serum,spinal fluid, lymph fluid, sections of the skin, respiratory,gastrointestinal, cardiovascular, and genitourinary tracts, tears,saliva, milk, blood cells, tumors, organs. Biological samples may be anytype of organismic tissue, including both healthy and diseased tissue(e.g., cancerous, malignant, necrotic, etc.). In certain embodiments,the biological sample is a liquid sample, such as blood or derivativethereof, e.g., plasma, tears, urine, semen, etc., where in someinstances the sample is a blood sample, including whole blood, such asblood obtained from venipuncture or fingerstick (where the blood may ormay not be combined with any reagents prior to assay, such aspreservatives, anticoagulants, etc.).

In certain embodiments the source of the sample is a “mammal” or“mammalian”, where these terms are used broadly to describe organismswhich are within the class mammalia, including the orders carnivore(e.g., dogs and cats), rodentia (e.g., mice, guinea pigs, and rats), andprimates (e.g., humans, chimpanzees, and monkeys). In some instances,the subjects are humans. The methods may be applied to samples obtainedfrom human subjects of both genders and at any stage of development(i.e., neonates, infant, juvenile, adolescent, adult), where in certainembodiments the human subject is a juvenile, adolescent or adult. Whilethe present invention may be applied to samples from a human subject, itis to be understood that the methods may also be carried-out on samplesfrom other animal subjects (that is, in “non-human subjects”) such as,but not limited to, birds, mice, rats, dogs, cats, livestock and horses.

In practicing the subject methods, a sample having particles (e.g., in aflow stream of a flow cytometer) is irradiated with light from a lightsource. In some embodiments, the light source is a broadband lightsource, emitting light having a broad range of wavelengths, such as forexample, spanning 50 nm or more, such as 100 nm or more, such as 150 nmor more, such as 200 nm or more, such as 250 nm or more, such as 300 nmor more, such as 350 nm or more, such as 400 nm or more and includingspanning 500 nm or more. For example, one suitable broadband lightsource emits light having wavelengths from 200 nm to 1500 nm. Anotherexample of a suitable broadband light source includes a light sourcethat emits light having wavelengths from 400 nm to 1000 nm. Wheremethods include irradiating with a broadband light source, broadbandlight source protocols of interest may include, but are not limited to,a halogen lamp, deuterium arc lamp, xenon arc lamp, stabilizedfiber-coupled broadband light source, a broadband LED with continuousspectrum, superluminescent emitting diode, semiconductor light emittingdiode, wide spectrum LED white light source, an multi-LED integratedwhite light source, among other broadband light sources or anycombination thereof.

In other embodiments, methods include irradiating with a narrow bandlight source emitting a particular wavelength or a narrow range ofwavelengths, such as for example with a light source which emits lightin a narrow range of wavelengths like a range of 50 nm or less, such as40 nm or less, such as 30 nm or less, such as 25 nm or less, such as 20nm or less, such as 15 nm or less, such as 10 nm or less, such as 5 nmor less, such as 2 nm or less and including light sources which emit aspecific wavelength of light (i.e., monochromatic light). Where methodsinclude irradiating with a narrow band light source, narrow band lightsource protocols of interest may include, but are not limited to, anarrow wavelength LED, laser diode or a broadband light source coupledto one or more optical bandpass filters, diffraction gratings,monochromators or any combination thereof.

Aspects of the present invention include collecting fluorescent lightwith a fluorescent light detector. A fluorescent light detector may, insome instances, be configured to detect fluorescence emissions fromfluorescent molecules, e.g., labeled specific binding members (such aslabeled antibodies that specifically bind to markers of interest)associated with the particle in the flow cell. In certain embodiments,methods include detecting fluorescence from the sample with one or morefluorescent light detectors, such as 2 or more, such as 3 or more, suchas 4 or more, such as 5 or more, such as 6 or more, such as 7 or more,such as 8 or more, such as 9 or more, such as 10 or more, such as 15 ormore and including 25 or more fluorescent light detectors. Inembodiments, each of the fluorescent light detectors is configured togenerate a fluorescence data signal. Fluorescence from the sample may bedetected by each fluorescent light detector, independently, over one ormore of the wavelength ranges of 200 nm-1200 nm. In some instances,methods include detecting fluorescence from the sample over a range ofwavelengths, such as from 200 nm to 1200 nm, such as from 300 nm to 1100nm, such as from 400 nm to 1000 nm, such as from 500 nm to 900 nm andincluding from 600 nm to 800 nm. In other instances, methods includedetecting fluorescence with each fluorescence detector at one or morespecific wavelengths. For example, the fluorescence may be detected atone or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm,625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm,780 nm, 785 nm, 647 nm, 617 nm and any combinations thereof, dependingon the number of different fluorescent light detectors in the subjectlight detection system. In certain embodiments, methods includedetecting wavelengths of light which correspond to the fluorescence peakwavelength of certain fluorochromes present in the sample. Inembodiments, fluorescent flow cytometer data is received from one ormore fluorescent light detectors (e.g., one or more detection channels),such as 2 or more, such as 3 or more, such as 4 or more, such as 5 ormore, such as 6 or more and including 8 or more fluorescent lightdetectors (e.g., 8 or more detection channels).

Systems for Classifying Fluorescent Flow Cytometer Data

Aspects of the present disclosure include systems for classifyingfluorescent flow cytometer data. In embodiments, fluorescent flowcytometer data is clustered, adjusted for spillover spreading, andpartitioned so that separate populations are classified differently. Insome embodiments, systems include a particle analyzer configured toproduce fluorescent flow cytometer data, and a processor configured toanalyze the fluorescent flow cytometer data.

In some embodiments, the subject particle analyzers have a flow cell,and a laser configured to irradiate particles in the flow cell. Inembodiments, the laser may be any convenient laser, such as a continuouswave laser. For example, the laser may be a diode laser, such as anultraviolet diode laser, a visible diode laser and a near-infrared diodelaser. In other embodiments, the laser may be a helium-neon (HeNe)laser. In some instances, the laser is a gas laser, such as ahelium-neon laser, argon laser, krypton laser, xenon laser, nitrogenlaser, CO2 laser, CO laser, argon-fluorine (ArF) excimer laser,krypton-fluorine (KrF) excimer laser, xenon chlorine (XeCI) excimerlaser or xenon-fluorine (XeF) excimer laser or a combination thereof. Inother instances, the subject flow cytometers include a dye laser, suchas a stilbene, coumarin or rhodamine laser. In yet other instances,lasers of interest include a metal-vapor laser, such as a helium-cadmium(HeCd) laser, helium-mercury (HeHg) laser, helium-selenium (HeSe) laser,helium-silver (HeAg) laser, strontium laser, neon-copper (NeCu) laser,copper laser or gold laser and combinations thereof. In still otherinstances, the subject flow cytometers include a solid-state laser, suchas a ruby laser, an Nd:YAG laser, NdCrYAG laser, Er:YAG laser, Nd:YLFlaser, Nd:YVO₄ laser, Nd:YCa₄O(BO₃)₃ laser, Nd:YCOB laser, titaniumsapphire laser, thulim YAG laser, ytterbium YAG laser, ytterbium₂O₃laser or cerium doped lasers and combinations thereof.

Aspects of the invention also include a forward scatter detectorconfigured to detect forward scattered light. The number of forwardscatter detectors in the subject flow cytometers may vary, as desired.For example, the subject particle analyzers may include 1 forwardscatter detector or multiple forward scatter detectors, such as 2 ormore, such as 3 or more, such as 4 or more, and including 5 or more. Incertain embodiments, flow cytometers include 1 forward scatter detector.In other embodiments, flow cytometers include 2 forward scatterdetectors.

Any convenient detector for detecting collected light may be used in theforward scatter detector described herein. Detectors of interest mayinclude, but are not limited to, optical sensors or detectors, such asactive-pixel sensors (APSs), avalanche photodiodes, image sensors,charge-coupled devices (CCDs), intensified charge-coupled devices(ICCDs), light emitting diodes, photon counters, bolometers,pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes,photomultiplier tubes (PMTs), phototransistors, quantum dotphotoconductors or photodiodes and combinations thereof, among otherdetectors. In certain embodiments, the collected light is measured witha charge-coupled device (CCD), semiconductor charge-coupled devices(CCD), active pixel sensors (APS), complementary metal-oxidesemiconductor (CMOS) image sensors or N-type metal-oxide semiconductor(NMOS) image sensors. In certain embodiments, the detector is aphotomultiplier tube, such as a photomultiplier tube having an activedetecting surface area of each region that ranges from 0.01 cm² to 10cm², such as from 0.05 cm² to 9 cm², such as from, such as from 0.1 cm²to 8 cm², such as from 0.5 cm² to 7 cm² and including from 1 cm² to 5cm².

Where the subject particle analyzers include multiple forward scatterdetectors, each detector may be the same, or the collection of detectorsmay be a combination of different types of detectors. For example, wherethe subject particle analyzers include two forward scatter detectors, insome embodiments the first forward scatter detector is a CCD-type deviceand the second forward scatter detector (or imaging sensor) is aCMOS-type device. In other embodiments, both the first and secondforward scatter detectors are CCD-type devices. In yet otherembodiments, both the first and second forward scatter detectors areCMOS-type devices. In still other embodiments, the first forward scatterdetector is a CCD-type device and the second forward scatter detector isa photomultiplier tube (PMT). In still other embodiments, the firstforward scatter detector is a CMOS-type device and the second forwardscatter detector is a photomultiplier tube. In yet other embodiments,both the first and second forward scatter detectors are photomultipliertubes.

In embodiments, the forward scatter detector is configured to measurelight continuously or in discrete intervals. In some instances,detectors of interest are configured to take measurements of thecollected light continuously. In other instances, detectors of interestare configured to take measurements in discrete intervals, such asmeasuring light every 0.001 millisecond, every 0.01 millisecond, every0.1 millisecond, every 1 millisecond, every 10 milliseconds, every 100milliseconds and including every 1000 milliseconds, or some otherinterval.

Embodiments of the invention also include a light dispersion/separatormodule positioned between the flow cell and the forward scatterdetector. Light dispersion devices of interest include but are notlimited to, colored glass, bandpass filters, interference filters,dichroic mirrors, diffraction gratings, monochromators and combinationsthereof, among other wavelength separating devices. In some embodiments,a bandpass filter is positioned between the flow cell and the forwardscatter detector. In other embodiments, more than one bandpass filter ispositioned between the flow cell and the forward scatter detector, suchas, for example, 2 or more, 3 or more, 4 or more, and including 5 ormore. In embodiments, the bandpass filters have a minimum bandwidthranging from 2 nm to 100 nm, such as from 3 nm to 95 nm, such as from 5nm to 95 nm, such as from 10 nm to 90 nm, such as from 12 nm to 85 nm,such as from 15 nm to 80 nm and including bandpass filters havingminimum bandwidths ranging from 20 nm to 50 nm. wavelengths and reflectslight with other wavelengths to the forward scatter detector.

Certain embodiments of the invention include a side scatter detectorconfigured to detect side scatter wavelengths of light (e.g., lightrefracted and reflected from the surfaces and internal structures of theparticle). In other embodiments, flow cytometers include multiple sidescatter detectors, such as 2 or more, such as 3 or more, such as 4 ormore, and including 5 or more.

Any convenient detector for detecting collected light may be used in theside scatter detector described herein. Detectors of interest mayinclude, but are not limited to, optical sensors or detectors, such asactive-pixel sensors (APSs), avalanche photodiodes, image sensors,charge-coupled devices (CCDs), intensified charge-coupled devices(ICCDs), light emitting diodes, photon counters, bolometers,pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes,photomultiplier tubes (PMTs), phototransistors, quantum dotphotoconductors or photodiodes and combinations thereof, among otherdetectors. In certain embodiments, the collected light is measured witha charge-coupled device (CCD), semiconductor charge-coupled devices(CCD), active pixel sensors (APS), complementary metal-oxidesemiconductor (CMOS) image sensors or N-type metal-oxide semiconductor(NMOS) image sensors. In certain embodiments, the detector is aphotomultiplier tube, such as a photomultiplier tube having an activedetecting surface area of each region that ranges from 0.01 cm² to 10cm², such as from 0.05 cm² to 9 cm², such as from, such as from 0.1 cm²to 8 cm², such as from 0.5 cm² to 7 cm² and including from 1 cm² to 5cm².

Where the subject particle analyzers include multiple side scatterdetectors, each side scatter detector may be the same, or the collectionof side scatter detectors may be a combination of different types ofdetectors. For example, where the subject particle analyzers include twoside scatter detectors, in some embodiments the first side scatterdetector is a CCD-type device and the second side scatter detector (orimaging sensor) is a CMOS-type device. In other embodiments, both thefirst and second side scatter detectors are CCD-type devices. In yetother embodiments, both the first and second side scatter detectors areCMOS-type devices. In still other embodiments, the first side scatterdetector is a CCD-type device and the second side scatter detector is aphotomultiplier tube (PMT). In still other embodiments, the first sidescatter detector is a CMOS-type device and the second side scatterdetector is a photomultiplier tube. In yet other embodiments, both thefirst and second side scatter detectors are photomultiplier tubes.

Embodiments of the invention also include a light dispersion/separatormodule positioned between the flow cell and the side scatter detector.Light dispersion devices of interest include but are not limited to,colored glass, bandpass filters, interference filters, dichroic mirrors,diffraction gratings, monochromators and combinations thereof, amongother wavelength separating devices.

In embodiments, the subject particle analyzers also include afluorescent light detector configured to detect one or more fluorescentwavelengths of light. In other embodiments, particle analyzers includemultiple fluorescent light detectors such as 2 or more, such as 3 ormore, such as 4 or more, 5 or more, 10 or more, 15 or more, andincluding 20 or more.

Any convenient detector for detecting collected light may be used in thefluorescent light detector described herein. Detectors of interest mayinclude, but are not limited to, optical sensors or detectors, such asactive-pixel sensors (APSs), avalanche photodiodes, image sensors,charge-coupled devices (CCDs), intensified charge-coupled devices(ICCDs), light emitting diodes, photon counters, bolometers,pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes,photomultiplier tubes (PMTs), phototransistors, quantum dotphotoconductors or photodiodes and combinations thereof, among otherdetectors. In certain embodiments, the collected light is measured witha charge-coupled device (CCD), semiconductor charge-coupled devices(CCD), active pixel sensors (APS), complementary metal-oxidesemiconductor (CMOS) image sensors or N-type metal-oxide semiconductor(NMOS) image sensors. In certain embodiments, the detector is aphotomultiplier tube, such as a photomultiplier tube having an activedetecting surface area of each region that ranges from 0.01 cm² to 10cm², such as from 0.05 cm² to 9 cm², such as from, such as from 0.1 cm²to 8 cm², such as from 0.5 cm² to 7 cm² and including from 1 cm² to 5cm².

Where the subject particle analyzers include multiple fluorescent lightdetectors, each fluorescent light detector may be the same, or thecollection of fluorescent light detectors may be a combination ofdifferent types of detectors. For example, where the subject particleanalyzers include two fluorescent light detectors, in some embodimentsthe first fluorescent light detector is a CCD-type device and the secondfluorescent light detector (or imaging sensor) is a CMOS-type device. Inother embodiments, both the first and second fluorescent light detectorsare CCD-type devices. In yet other embodiments, both the first andsecond fluorescent light detectors are CMOS-type devices. In still otherembodiments, the first fluorescent light detector is a CCD-type deviceand the second fluorescent light detector is a photomultiplier tube(PMT). In still other embodiments, the first fluorescent light detectoris a CMOS-type device and the second fluorescent light detector is aphotomultiplier tube. In yet other embodiments, both the first andsecond fluorescent light detectors are photomultiplier tubes.

Embodiments of the invention also include a light dispersion/separatormodule positioned between the flow cell and the fluorescent lightdetector. Light dispersion devices of interest include but are notlimited to, colored glass, bandpass filters, interference filters,dichroic mirrors, diffraction gratings, monochromators and combinationsthereof, among other wavelength separating devices.

In embodiments of the present disclosure, fluorescent light detectors ofinterest are configured to measure collected light at one or morewavelengths, such as at 2 or more wavelengths, such as at 5 or moredifferent wavelengths, such as at 10 or more different wavelengths, suchas at 25 or more different wavelengths, such as at 50 or more differentwavelengths, such as at 100 or more different wavelengths, such as at200 or more different wavelengths, such as at 300 or more differentwavelengths and including measuring light emitted by a sample in theflow stream at 400 or more different wavelengths. In some embodiments, 2or more detectors in a flow cytometer as described herein are configuredto measure the same or overlapping wavelengths of collected light.

In some embodiments, fluorescent light detectors of interest areconfigured to measure collected light over a range of wavelengths (e.g.,200 nm-1000 nm). In certain embodiments, detectors of interest areconfigured to collect spectra of light over a range of wavelengths. Forexample, particle analyzers may include one or more detectors configuredto collect spectra of light over one or more of the wavelength ranges of200 nm-1000 nm. In yet other embodiments, detectors of interest areconfigured to measure light emitted by a sample in the flow stream atone or more specific wavelengths. For example, particle analyzers mayinclude one or more detectors configured to measure light at one or moreof 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785nm, 647 nm, 617 nm and any combinations thereof. In certain embodiments,one or more detectors may be configured to be paired with specificfluorophores, such as those used with the sample in a fluorescenceassay.

Suitable flow cytometry systems may include, but are not limited tothose described in Ormerod (ed.), Flow Cytometry: A Practical Approach,Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow CytometryProtocols, Methods in Molecular Biology No. 91, Humana Press (1997);Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et al.(2012) Ann Clin Biochem. January; 49(pt 1):17-28; Linden, et. al., SeminThrom Hemost. 2004 October; 30(5):502-11; Alison, et al. J Pathol, 2010December; 222(4):335-344; and Herbig, et al. (2007) Crit Rev Ther DrugCarrier Syst. 24(3):203-255; the disclosures of which are incorporatedherein by reference. In certain instances, flow cytometry systems ofinterest include BD Biosciences FACSCanto™ flow cytometer, BDBiosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BDAccuri™ C6 Plus flow cytometer, BD Biosciences FACSCelesta™ flowcytometer, BD Biosciences FACSLyric™ flow cytometer, BD BiosciencesFACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer,BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortessa™X-20 flow cytometer, BD Biosciences FACSPresto™ flow cytometer, BDBiosciences FACSVia™ flow cytometer and BD Biosciences FACSCalibur™ cellsorter, a BD Biosciences FACSCount™ cell sorter, BD BiosciencesFACSLyric™ cell sorter, BD Biosciences Via™ cell sorter, BD BiosciencesInflux™ cell sorter, BD Biosciences Jazz™ cell sorter, BD BiosciencesAria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BDBiosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusioncell sorter and BD Biosciences FACSMelody™ cell sorter, BD BiosciencesFACSymphony™ S6 cell sorter or the like.

In some embodiments, the subject systems are flow cytometric systems,such those described in U.S. Pat. Nos. 10,663,476; 10,620,111;10,613,017; 10,605,713; 10,585,031; 10,578,542; 10,578,469; 10,481,074;10,302,545; 10,145,793; 10,113,967; 10,006,852; 9,952,076; 9,933,341;9,726,527; 9,453,789; 9,200,334; 9,097,640; 9,095,494; 9,092,034;8,975,595; 8,753,573; 8,233,146; 8,140,300; 7,544,326; 7,201,875;7,129,505; 6,821,740; 6,813,017; 6,809,804; 6,372,506; 5,700,692;5,643,796; 5,627,040; 5,620,842; 5,602,039; 4,987,086; 4,498,766; thedisclosures of which are herein incorporated by reference in theirentirety.

In certain instances, flow cytometry systems of the invention areconfigured for imaging particles in a flow stream by fluorescenceimaging using radiofrequency tagged emission (FIRE), such as thosedescribed in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013)as well as described in U.S. Pat. Nos. 9,423,353; 9,784,661; 9,983,132;10,006,852; 10,078,045; 10,036,699; 10,222,316; 10,288,546; 10,324,019;10,408,758; 10,451,538; 10,620,111; and U.S. Patent Publication Nos.2017/0133857; 2017/0328826; 2017/0350803; 2018/0275042; 2019/0376895 and2019/0376894 the disclosures of which are herein incorporated byreference.

In certain embodiments, the subject systems additionally include aprocessor having memory operably coupled to the processor wherein thememory includes instructions stored thereon, which when executed by theprocessor, cause the processor to cluster fluorescent flow cytometerdata, determine a measure of spillover spreading, adjust the fluorescentflow cytometer data for spillover spreading, and partition the spilloverspreading adjusted fluorescent flow cytometer data.

In some embodiments, the processor is configured to generate one or morepopulation clusters based on the determined parameters of analytes(e.g., cells, particles) in the sample. In embodiments, fluorescent flowcytometer data includes signals from a plurality of differentfluorochromes, such as, for instance, ranging from 2 to 20 differentfluorochromes, and including 3 to 5 different fluorochromes. In someembodiments, a plurality of different fluorochromes includes 2 or moredifferent fluorochromes, including 3 or more, 4 or more, 5 or more, 6 ormore, 7 or more, 8 or more, 9 or more, 10 or more, 11, or more, 12 ormore, 13 or more, 14 or more 15 or more, and including 20 or moredifferent fluorochromes. Thus, populations are recognized as clusters inthe data. Conversely, each data cluster generally is interpreted ascorresponding to a population of a particular type of cell or analyte,although clusters that correspond to noise or background typically alsoare observed. A cluster may be defined in a subset of the dimensions,e.g., with respect to a subset of the measured fluorescent parameters(i.e., fluorochromes), which corresponds to populations that differ inonly a subset of the measured parameters or features extracted from themeasurements of the sample.

In embodiments, the processor contains instructions for performing thesupervised clustering of fluorescent flow cytometer data. As such, incertain embodiments, populations of fluorescent flow cytometer data areclustered based on each data point's status relative to a hierarchy. Insome embodiments, the hierarchy establishes the manner in which datapoints that are positive or negative for the same fluorochromes aregrouped together. In some embodiments, clustering fluorescent flowcytometer data includes comparing the fluorescent flow cytometer data toa threshold. In other words, fluorescent flow cytometer data that liesabove the threshold value may be described as positive for a particularfluorochrome, while fluorescent flow cytometer data that lies below thethreshold value may be described as negative for the same fluorochrome.

After flow cytometer data is clustered, the processor may be configuredto determine a measure of spillover spreading for the populations offluorescent flow cytometer data. In some embodiments, unadjusted flowcytometer data contains noise (i.e., spillover spreading) due to theunintentional detection of certain wavelengths of light by one or moredetectors. In certain embodiments, determining a measure of spilloverspreading includes quantifying the extent to which the intensity oflight collected from a first fluorochrome by a first detector isimpacted by the simultaneous collection of light from a secondfluorochrome by the same detector. In certain embodiments, determining ameasure of spillover spreading includes calculating a spilloverspreading coefficient. In some embodiments, the spillover spreadingcoefficient is calculated according to Equation 1:

${S\; S} = {\frac{\Delta\sigma_{f}}{\sqrt{\Delta d}} = \frac{\sqrt{\left( \sigma_{pos} \right)^{2} - \left( \sigma_{neg} \right)^{2}}}{\sqrt{d_{pos} - d_{neg}}}}$

As shown in Equation 1, SS is the spillover spreading coefficient,Δσ_(f) is an incremental standard deviation indicating the spread of theemission between the positive and negative fluorescent flow cytometerdata collected from a fluorochrome, and Δd is a difference in theintensity of the fluorescent light between the positive and negativefluorescent flow cytometer data received by a fluorescent lightdetector. As such, the spillover spreading coefficient measures theextent to which fluorescent flow cytometer data collected by a givenfluorescent light detector is impacted by the presence of lightassociated with a particular fluorochrome. In other words, the spilloverspreading coefficient estimates the error (i.e., noise) contributed tothe fluorescent flow cytometer data by light emitting from the relevantfluorochrome being collected by a given detector. In embodiments, ahigher spillover spreading coefficient corresponds to more spilloverspreading for a given fluorochrome-detector pair.

In some embodiments, determining a measure of spillover spreading alsoincludes calculating spillover spreading coefficients for each possiblefluorescent light detector-fluorochrome combination so that it can bedetermined how fluorescent flow cytometer data collected at eachdetector is affected by the presence of light associated with eachfluorochrome. In embodiments, spillover spreading coefficientscalculated for each fluorescent light detector-fluorochrome pair arecombined in a spillover spreading matrix. In certain embodiments, thespillover spreading matrix demonstrates how the detection of aparticular fluorochrome by its corresponding detector is impacted byspillover from other fluorochromes. The cell in which a column and rowintersects is populated with a spillover spreading coefficientcalculated for that fluorescent light detector-fluorochrome pairindicating the extent to which the fluorochrome in question contributeserror at the relevant detector. The total degree to which a fluorochromecauses spillover spreading can be approximated by summing all the valuesin its row, and the total degree to which a detector is impacted byspillover spreading can be calculated by summing all the values in itscolumn. In some embodiments, spillover spreading coefficients are summedin order to calculate the total spreading effect (i.e., the cumulativeeffect of spillover spreading on a particular subset of fluorescent flowcytometer data).

In some embodiments, the spillover spreading matrix is calculated bymeans of the AutoSpread algorithm. The AutoSpread algorithm isconfigured to create a spillover spreading matrix (e.g., as describedabove) without requiring a distinction between populations of flowcytometer that are positive and negative with respect to a givenfluorochrome. AutoSpread characterizes the spread contributed to thedetected signal of a first fluorochrome by the inclusion of a secondfluorochrome in the same flow cytometry panel. AutoSpread produces onecoefficient for each interaction between a fluorescent light detectorand a fluorochrome, and arranges the coefficients into a matrix akin tothe Spillover spreading matrix described above. In embodiments,calculating a spillover spreading coefficient includes assuming that theintensity of fluorescent light collected by the fluorescent lightdetector for the negative population of flow cytometer data is zero, andthe corresponding standard deviation is an unknown quantity. In someembodiments, the spillover spreading coefficient is calculated accordingto Equation 2:

${S\; S} = \frac{\sqrt{\sigma^{2} - \sigma_{0}^{2}}}{\sqrt{d}}$

As shown in Equation 2, SS is the spillover spreading coefficient, σ² isthe standard deviation of the positive population of fluorescent flowcytometer data, σ₀ ² is an estimate of the standard deviation of thenegative population of fluorescent flow cytometer data, and d is theintensity of light collected by a fluorescent light detector. In someembodiments, in order to obtain an estimate of the standard deviation ofthe negative population of fluorescent flow cytometer data (σ₀ ²) whenthe intensity of fluorescent light collected by the fluorescent lightdetector for the negative population of flow cytometer data is assumedto be zero, the spillover spreading coefficient is calculated followinga sequence of linear regressions. Fluorescent flow cytometer data isfirst sorted into quantiles according to intensity values that aredetected by the fluorescent light detector. The number of quantiles isby default 256, but is adjusted downwards to as few as 8 to ensure thateach quantile has a sufficient number of data points to allow forreliable estimation of standard deviations. Next, the robust standarddeviation of the light emitted from the fluorochrome is regressedagainst the square root of the median intensity of light detected foreach quantile. The y-intercept of the ordinary least squares fit istaken as the estimate of the standard deviation in the negativepopulation of flow cytometer data when the intensity of light detectedfor the negative population is assumed to be zero. The estimate of thestandard deviation of light emitted from the fluorochrome is used toobtain new zero-adjusted standard deviations. The zero-adjusted standarddeviation for the fluorochrome is regressed against the square root ofthe median fluorescence of each quantile detected by the fluorescentlight detector. The slope of the ordinary least squares fit (calculatedby Equation 2) is taken as the spillover spreading coefficient.

In some embodiments, the processor is configured to adjust fluorescentflow cytometer data to account for spillover spreading. In someembodiments, flow cytometer data is adjusted to eliminate constructiveerror from spillover spreading. In embodiments, adjusting fluorescentflow cytometer data includes generating distinct spillover spreadingadjusted populations. In certain embodiments, generating distinctspillover spreading adjusted populations includes subtracting themagnitude of the spillover spreading from the relevant population(s) offlow cytometer data, i.e., to counteract the effects of signals beingincreased due to constructive spillover spreading error. In certainembodiments, the magnitude of spillover spreading is determined from thespillover spreading matrix. In some embodiments, adjusting flowcytometer data includes subtracting the total spreading effect from therelevant portion of the flow cytometer data.

After populations of fluorescent flow cytometer data have been adjustedfor spillover spreading (e.g., as described above), the processor may beconfigured to partition the spillover spreading adjusted populations offluorescent flow cytometer data. Partitions are created to formallydistinguish between populations of fluorescent flow cytometer data thatare to be classified differently (e.g., represent different phenotypes).In embodiments, partitioning distinct spillover spreading adjustedpopulations of flow cytometer data includes calculating Matthew'scorrelation coefficient. In embodiments, potential partitions betweendistinct spillover spreading adjusted populations of fluorescent flowcytometer data are evaluated with respect to each of the thresholdvalues. In such embodiments, Matthew's correlation coefficient iscalculated for each of the threshold values, thereby assessing the levelof agreement between the threshold and the partition. Embodiments of theinvention therefore involve partitioning the populations such thatMatthew's correlation coefficient is optimized with respect to therelevant thresholds distinguishing between positive and negativepopulations of flow cytometer data with respect to a particularfluorochrome. In other words, partitioning fluorescent flow cytometerdata includes maximizing the extent to which separate populations (i.e.,populations that exhibit different combinations of fluorescentparameters) are distinguished from each other as determined by theirrelationship to relevant thresholds (e.g., quantified by Matthew'scorrelation coefficient). In embodiments, the processor iterates thisprocess to determine optimal partitions distinguishing between positiveand negative populations for each relevant parameter of flow cytometerdata. In embodiments, Matthew's correlation coefficient is calculatedaccording to Equation 3:

${M\; C\; C} = \frac{{TP \times TN} - {FP \times FN}}{\sqrt{\left( {{TP} + {FP}} \right)\left( {{TP} + {FN}} \right)\left( {{TN} + {FP}} \right)\left( {{TN} + {FN}} \right)}}$

As shown in Equation 3, MCC is Matthew's correlation coefficient, TPrepresents true positive events, TN represents true negative events, FPrepresents false positive events and FN represents false negativeevents. According to the present disclosure, true positive events arefluorescent flow cytometer data that are assessed to be positive for aparticular fluorochrome by both the threshold and the partition, truenegative events are fluorescent flow cytometer data that are assessed tobe negative for a particular fluorochrome by both the threshold and thepartition, false positive events are fluorescent flow cytometer datathat are assessed to be positive for a particular fluorochrome by thepartition and negative by the threshold, and false negative events areflow cytometer data that are assessed to be negative for a particularfluorochrome by the partition and positive by the threshold.

In some embodiments of the invention, fluorescent flow cytometer datadoes not contain signals that are positive for a particularfluorochrome. In other words, fluorescent light emitting from thefluorochrome is not detected. In such embodiments, partitioning thedistinct populations of spillover spreading adjusted fluorescent flowcytometer data may comprise calculating the balanced accuracy of eachpartition as an alternative metric for determining the optimalpartition. Balanced accuracy is the average of the accuracy ofdetermining positive events and the accuracy of determining negativeevents and is calculated according to Equation 4:

${B\; A} = \frac{\left( \frac{TP}{{TP} + {FN}} \right) + \left( \frac{TN}{{TN} + {FP}} \right)}{2}$

As shown in Equation 4, BA is balanced accuracy, TP represents truepositive events, TN represents true negative events, FP represents falsepositive events and FN represents false negative events.

Embodiments of the invention also include classifying the partitionedpopulations of fluorescent flow cytometer data, i.e., determining thesubtype of cells or particles designated by each distinct spilloverspreading adjusted population of flow cytometer data. In embodiments,classifications are determined based on the hierarchy (described above).As such, a population of fluorescent flow cytometer data that has beenpartitioned (e.g., as described above) is assigned a classification(i.e., phenotyped) based on the combination of fluorescent parameters itexhibits.

FIG. 8 shows a system 800 for flow cytometry in accordance with anillustrative embodiment of the present invention. The system 800includes a flow cytometer 810, a controller/processor 890 and a memory895. The flow cytometer 810 includes one or more excitation lasers 815a-815 c, a focusing lens 820, a flow chamber 825, a forward scatterdetector 830, a side scatter detector 835, a fluorescence collectionlens 840, one or more beam splitters 845 a-845 g, one or more bandpassfilters 850 a-850 e, one or more longpass (“LP”) filters 855 a-855 b,and one or more fluorescent light detectors 860 a-860 f.

The excitation lasers 815 a-c emit light in the form of a laser beam.The wavelengths of the laser beams emitted from excitation lasers 815a-815 c are 488 nm, 633 nm, and 325 nm, respectively, in the examplesystem of FIG. 8. The laser beams are first directed through one or moreof beam splitters 845 a and 845 b. Beam splitter 845 a transmits lightat 488 nm and reflects light at 633 nm. Beam splitter 845 b transmits UVlight (light with a wavelength in the range of 10 to 400 nm) andreflects light at 488 nm and 633 nm.

The laser beams are then directed to a focusing lens 820, which focusesthe beams onto the portion of a fluid stream where particles of a sampleare located, within the flow chamber 825. The flow chamber is part of afluidics system which directs particles, typically one at a time, in astream to the focused laser beam for interrogation. The flow chamber cancomprise a flow cell in a benchtop cytometer or a nozzle tip in astream-in-air cytometer.

The light from the laser beam(s) interacts with the particles in thesample by diffraction, refraction, reflection, scattering, andabsorption with re-emission at various different wavelengths dependingon the characteristics of the particle such as its size, internalstructure, and the presence of one or more fluorescent moleculesattached to or naturally present on or in the particle. The fluorescenceemissions as well as the diffracted light, refracted light, reflectedlight, and scattered light may be routed to one or more of the forwardscatter detector 830, side scatter detector 835, and the one or morefluorescent light detectors 860 a-860 f through one or more of the beamsplitters 845 a-845 g, the bandpass filters 850 a-850 e, the longpassfilters 855 a-855 b, and the fluorescence collection lens 840.

The fluorescence collection lens 840 collects light emitted from theparticle-laser beam interaction and routes that light towards one ormore beam splitters and filters. Bandpass filters, such as bandpassfilters 850 a-850 e, allow a narrow range of wavelengths to pass throughthe filter. For example, bandpass filter 850 a is a 510/20 filter. Thefirst number represents the center of a spectral band. The second numberprovides a range of the spectral band. Thus, a 510/20 filter extends 10nm on each side of the center of the spectral band, or from 500 nm to520 nm. Shortpass filters transmit wavelengths of light equal to orshorter than a specified wavelength. Longpass filters, such as longpassfilters 855 a-855 b, transmit wavelengths of light equal to or longerthan a specified wavelength of light. For example, longpass filter 855a, which is a 670 nm longpass filter, transmits light equal to or longerthan 670 nm. Filters are often selected to optimize the specificity of adetector for a particular fluorescent dye. The filters can be configuredso that the spectral band of light transmitted to the detector is closeto the emission peak of a fluorescent dye.

Beam splitters direct light of different wavelengths in differentdirections. Beam splitters can be characterized by filter propertiessuch as shortpass and longpass. For example, beam splitter 805 g is a620 SP beam splitter, meaning that the beam splitter 845 g transmitswavelengths of light that are 620 nm or shorter and reflects wavelengthsof light that are longer than 620 nm in a different direction. In oneembodiment, the beam splitters 845 a-845 g can comprise optical mirrors,such as dichroic mirrors.

The forward scatter detector 830 is positioned off axis from the directbeam through the flow cell and is configured to detect diffracted light,the excitation light that travels through or around the particle inmostly a forward direction. The intensity of the light detected by theforward scatter detector is dependent on the overall size of theparticle. The forward scatter detector can include a photodiode. Theside scatter detector 835 is configured to detect refracted andreflected light from the surfaces and internal structures of theparticle, and tends to increase with increasing particle complexity ofstructure. The fluorescence emissions from fluorescent moleculesassociated with the particle can be detected by the one or morefluorescent light detectors 860 a-860 f. The side scatter detector 835and fluorescent light detectors can include photomultiplier tubes. Thesignals detected at the forward scatter detector 830, the side scatterdetector 835 and the fluorescent detectors can be converted toelectronic signals (voltages) by the detectors. This data can provideinformation about the sample.

In operation, cytometer operation is controlled by acontroller/processor 890, and the measurement data from the detectorscan be stored in the memory 895 and processed by thecontroller/processor 890. Although not shown explicitly, thecontroller/processor 890 is coupled to the detectors to receive theoutput signals therefrom, and may also be coupled to electrical andelectromechanical components of the flow cytometer 800 to control thelasers, fluid flow parameters, and the like. Input/output (I/O)capabilities 897 may be provided also in the system. The memory 895,controller/processor 890, and I/O 897 may be entirely provided as anintegral part of the flow cytometer 810. In such an embodiment, adisplay may also form part of the I/O capabilities 897 for presentingexperimental data to users of the cytometer 800. Alternatively, some orall of the memory 895 and controller/processor 890 and I/O capabilitiesmay be part of one or more external devices such as a general purposecomputer. In some embodiments, some or all of the memory 895 andcontroller/processor 890 can be in wireless or wired communication withthe cytometer 810. The controller/processor 890 in conjunction with thememory 895 and the I/O 897 can be configured to perform variousfunctions related to the preparation and analysis of a flow cytometerexperiment.

The system illustrated in FIG. 8 includes six different detectors thatdetect fluorescent light in six different wavelength bands (which may bereferred to herein as a “filter window” for a given detector) as definedby the configuration of filters and/or splitters in the beam path fromthe flow cell 825 to each detector. Different fluorescent molecules usedfor a flow cytometer experiment will emit light in their owncharacteristic wavelength bands. The particular fluorescent labels usedfor an experiment and their associated fluorescent emission bands may beselected to generally coincide with the filter windows of the detectors.However, as more detectors are provided, and more labels are utilized,perfect correspondence between filter windows and fluorescent emissionspectra is not possible. It is generally true that although the peak ofthe emission spectra of a particular fluorescent molecule may lie withinthe filter window of one particular detector, some of the emissionspectra of that label will also overlap the filter windows of one ormore other detectors. This may be referred to as spillover. The I/O 897can be configured to receive data regarding a flow cytometer experimenthaving a panel of fluorescent labels and a plurality of cell populationshaving a plurality of markers, each cell population having a subset ofthe plurality of markers. The I/O 897 can also be configured to receivebiological data assigning one or more markers to one or more cellpopulations, marker density data, emission spectrum data, data assigninglabels to one or more markers, and cytometer configuration data. Flowcytometer experiment data, such as label spectral characteristics andflow cytometer configuration data can also be stored in the memory 895.The controller/processor 890 can be configured to evaluate one or moreassignments of labels to markers.

One of skill in the art will recognize that a flow cytometer inaccordance with an embodiment of the present invention is not limited tothe flow cytometer depicted in FIG. 8, but can include any flowcytometer known in the art. For example, a flow cytometer may have anynumber of lasers, beam splitters, filters, and detectors at variouswavelengths and in various different configurations.

FIG. 9 shows a functional block diagram for one example of a processor900, for analyzing and displaying data. A processor 900 can beconfigured to implement a variety of processes for controlling graphicdisplay of biological events. A flow cytometer 902 can be configured toacquire fluorescent flow cytometer data by analyzing a biological sample(e.g., as described above). The apparatus can be configured to providebiological event data to the processor 900. A data communication channelcan be included between the flow cytometer 902 and the processor 900.The data can be provided to the processor 900 via the data communicationchannel. The processor 900 can be configured to provide a graphicaldisplay including plots (e.g., as described above) to display 906. Theprocessor 900 can be further configured to render a gate aroundpopulations of fluorescent flow cytometer data shown by the displaydevice 906, overlaid upon the plot, for example. In some embodiments,the gate can be a logical combination of one or more graphical regionsof interest drawn upon a single parameter histogram or bivariate plot.In some embodiments, the display can be used to display analyteparameters or saturated detector data.

The processor 900 can be further configured to display fluorescent flowcytometer data on the display device 906 within the gate differentlyfrom other events in the fluorescent flow cytometer data outside of thegate. For example, the processor 900 can be configured to render thecolor of fluorescent flow cytometer data contained within the gate to bedistinct from the color of fluorescent flow cytometer data outside ofthe gate. In this way, the processor 900 may be configured to renderdifferent colors to represent each unique population of data. Thedisplay device 906 can be implemented as a monitor, a tablet computer, asmartphone, or other electronic device configured to present graphicalinterfaces.

The processor 900 can be configured to receive a gate selection signalidentifying the gate from a first input device. For example, the firstinput device can be implemented as a mouse 910. The mouse 910 caninitiate a gate selection signal to the processor 900 identifying thepopulation to be displayed on or manipulated via the display device 906(e.g., by clicking on or in the desired gate when the cursor ispositioned there). In some implementations, the first device can beimplemented as the keyboard 908 or other means for providing an inputsignal to the processor 900 such as a touchscreen, a stylus, an opticaldetector, or a voice recognition system. Some input devices can includemultiple inputting functions. In such implementations, the inputtingfunctions can each be considered an input device. For example, as shownin FIG. 9, the mouse 910 can include a right mouse button and a leftmouse button, each of which can generate a triggering event.

The triggering event can cause the processor 900 to alter the manner inwhich the fluorescent flow cytometer data is displayed, which portionsof the data is actually displayed on the display device 906, and/orprovide input to further processing such as selection of a population ofinterest for analysis.

In some embodiments, the processor 900 can be configured to detect whengate selection is initiated by the mouse 910. The processor 900 can befurther configured to automatically modify plot visualization tofacilitate the gating process. The modification can be based on thespecific distribution of data received by the processor 900.

The processor 900 can be connected to a storage device 904. The storagedevice 904 can be configured to receive and store data from theprocessor 900. The storage device 904 can be further configured to allowretrieval of data, such as fluorescent flow cytometer data, by theprocessor 900.

A display device 906 can be configured to receive display data from theprocessor 900. The display data can comprise plots of fluorescent flowcytometer data and gates outlining sections of the plots. The displaydevice 906 can be further configured to alter the information presentedaccording to input received from the processor 900 in conjunction withinput from apparatus 902, the storage device 904, the keyboard 908,and/or the mouse 910.

In some implementations the processor 900 can generate a user interfaceto receive example events for sorting. For example, the user interfacecan include a control for receiving example events or example images.The example events or images or an example gate can be provided prior tocollection of event data for a sample, or based on an initial set ofevents for a portion of the sample.

Computer-Controlled Systems

Aspects of the present disclosure further include computer-controlledsystems, where the systems further include one or more computers forcomplete automation or partial automation. In some embodiments, systemsinclude a computer having a computer readable storage medium with acomputer program stored thereon, where the computer program when loadedon the computer includes instructions for clustering fluorescent flowcytometer data into populations according to one or more differentparameters (i.e., fluorochromes), determining the spillover spreadingbetween each detector-parameter pair (i.e., by calculating spilloverspreading coefficients), creating a spillover spreading matrixdemonstrating how the detection of a particular parameter by itscorresponding detector is impacted by spillover from other parameters,altering the fluorescent flow cytometer data to compensate for spilloverspreading by subtracting the magnitude of the spillover spreading asdetermined by the spillover spreading matrix, evaluating the quality ofdifferent partitions separating distinct populations of fluorescent flowcytometer data by calculating Matthew's correlation coefficient withrespect to thresholds distinguishing between populations that arepositive for a given parameter and population that are negative for agiven parameter, and classifying (i.e., phenotyping) adjustedpopulations of fluorescent flow cytometer data.

In embodiments, the system is configured to analyze the data within asoftware or an analysis tool for analyzing flow cytometer data ornucleic acid sequence data, such as FlowJo® (Ashland, Oreg.). FlowJo® isa software package developed by FlowJo LLC (a subsidiary of BectonDickinson) for analyzing flow cytometer data. The software is configuredto manage flow cytometer data and produce graphical reports thereon(https://www(dot)flowjo(dot)com/learn/flowjo-university/flowjo). Theinitial data can be analyzed within the data analysis software or tool(e.g., FlowJo®) by appropriate means, such as manual gating, clusteranalysis, or other computational techniques. The instant systems, or aportion thereof, can be implemented as software components of a softwarefor analyzing data, such as FlowJo®. In these embodiments,computer-controlled systems according to the instant disclosure mayfunction as a software “plugin” for an existing software package, suchas FlowJo®.

In embodiments, the system includes an input module, a processing moduleand an output module. The subject systems may include both hardware andsoftware components, where the hardware components may take the form ofone or more platforms, e.g., in the form of servers, such that thefunctional elements, i.e., those elements of the system that carry outspecific tasks (such as managing input and output of information,processing information, etc.) of the system may be carried out by theexecution of software applications on and across the one or morecomputer platforms represented of the system.

Systems may include a display and operator input device. Operator inputdevices may, for example, be a keyboard, mouse, or the like. Theprocessing module includes a processor which has access to a memoryhaving instructions stored thereon for performing the steps of thesubject methods. The processing module may include an operating system,a graphical user interface (GUI) controller, a system memory, memorystorage devices, and input-output controllers, cache memory, a databackup unit, and many other devices. The processor may be a commerciallyavailable processor, or it may be one of other processors that are orwill become available. The processor executes the operating system andthe operating system interfaces with firmware and hardware in awell-known manner, and facilitates the processor in coordinating andexecuting the functions of various computer programs that may be writtenin a variety of programming languages, such as Java, Perl, C++, otherhigh level or low level languages, as well as combinations thereof, asis known in the art. The operating system, typically in cooperation withthe processor, coordinates and executes functions of the othercomponents of the computer. The operating system also providesscheduling, input-output control, file and data management, memorymanagement, and communication control and related services, all inaccordance with known techniques. The processor may be any suitableanalog or digital system. In some embodiments, processors include analogelectronics which allows the user to manually align a light source withthe flow stream based on the first and second light signals. In someembodiments, the processor includes analog electronics which providefeedback control, such as for example negative feedback control.

The system memory may be any of a variety of known or future memorystorage devices. Examples include any commonly available random accessmemory (RAM), magnetic medium such as a resident hard disk or tape, anoptical medium such as a read and write compact disc, flash memorydevices, or other memory storage device. The memory storage device maybe any of a variety of known or future devices, including a compact diskdrive, a tape drive, a removable hard disk drive, or a diskette drive.Such types of memory storage devices typically read from, and/or writeto, a program storage medium (not shown) such as, respectively, acompact disk, magnetic tape, removable hard disk, or floppy diskette.Any of these program storage media, or others now in use or that maylater be developed, may be considered a computer program product. Aswill be appreciated, these program storage media typically store acomputer software program and/or data. Computer software programs, alsocalled computer control logic, typically are stored in system memoryand/or the program storage device used in conjunction with the memorystorage device.

In some embodiments, a computer program product is described comprisinga computer usable medium having control logic (computer softwareprogram, including program code) stored therein. The control logic, whenexecuted by the processor the computer, causes the processor to performfunctions described herein. In other embodiments, some functions areimplemented primarily in hardware using, for example, a hardware statemachine. Implementation of the hardware state machine so as to performthe functions described herein will be apparent to those skilled in therelevant arts.

Memory may be any suitable device in which the processor can store andretrieve data, such as magnetic, optical, or solid-state storage devices(including magnetic or optical disks or tape or RAM, or any othersuitable device, either fixed or portable). The processor may include ageneral-purpose digital microprocessor suitably programmed from acomputer readable medium carrying necessary program code. Programmingcan be provided remotely to processor through a communication channel,or previously saved in a computer program product such as memory or someother portable or fixed computer readable storage medium using any ofthose devices in connection with memory. For example, a magnetic oroptical disk may carry the programming, and can be read by a diskwriter/reader. Systems of the invention also include programming, e.g.,in the form of computer program products, algorithms for use inpracticing the methods as described above. Programming according to thepresent invention can be recorded on computer readable media, e.g., anymedium that can be read and accessed directly by a computer. Such mediainclude, but are not limited to: magnetic storage media, such as floppydiscs, hard disc storage medium, and magnetic tape; optical storagemedia such as CD-ROM; electrical storage media such as RAM and ROM;portable flash drive; and hybrids of these categories such asmagnetic/optical storage media.

The processor may also have access to a communication channel tocommunicate with a user at a remote location. By remote location ismeant the user is not directly in contact with the system and relaysinput information to an input manager from an external device, such as acomputer connected to a Wide Area Network (“WAN”), telephone network,satellite network, or any other suitable communication channel,including a mobile telephone (i.e., smartphone).

In some embodiments, systems according to the present disclosure may beconfigured to include a communication interface. In some embodiments,the communication interface includes a receiver and/or transmitter forcommunicating with a network and/or another device. The communicationinterface can be configured for wired or wireless communication,including, but not limited to, radio frequency (RF) communication (e.g.,Radio-Frequency Identification (RFID), Zigbee communication protocols,WiFi, infrared, wireless Universal Serial Bus (USB), Ultra Wide Band(UWB), Bluetooth® communication protocols, and cellular communication,such as code division multiple access (CDMA) or Global System for Mobilecommunications (GSM).

In one embodiment, the communication interface is configured to includeone or more communication ports, e.g., physical ports or interfaces suchas a USB port, an RS-232 port, or any other suitable electricalconnection port to allow data communication between the subject systemsand other external devices such as a computer terminal (for example, ata physician's office or in hospital environment) that is configured forsimilar complementary data communication.

In one embodiment, the communication interface is configured forinfrared communication, Bluetooth® communication, or any other suitablewireless communication protocol to enable the subject systems tocommunicate with other devices such as computer terminals and/ornetworks, communication enabled mobile telephones, personal digitalassistants, or any other communication devices which the user may use inconjunction.

In one embodiment, the communication interface is configured to providea connection for data transfer utilizing Internet Protocol (IP) througha cell phone network, Short Message Service (SMS), wireless connectionto a personal computer (PC) on a Local Area Network (LAN) which isconnected to the internet, or WiFi connection to the internet at a WiFihotspot.

In one embodiment, the subject systems are configured to wirelesslycommunicate with a server device via the communication interface, e.g.,using a common standard such as 802.11 or Bluetooth® RF protocol, or anIrDA infrared protocol. The server device may be another portabledevice, such as a smart phone, Personal Digital Assistant (PDA) ornotebook computer; or a larger device such as a desktop computer,appliance, etc. In some embodiments, the server device has a display,such as a liquid crystal display (LCD), as well as an input device, suchas buttons, a keyboard, mouse or touch-screen.

In some embodiments, the communication interface is configured toautomatically or semi-automatically communicate data stored in thesubject systems, e.g., in an optional data storage unit, with a networkor server device using one or more of the communication protocols and/ormechanisms described above.

Output controllers may include controllers for any of a variety of knowndisplay devices for presenting information to a user, whether a human ora machine, whether local or remote. If one of the display devicesprovides visual information, this information typically may be logicallyand/or physically organized as an array of picture elements. A graphicaluser interface (GUI) controller may include any of a variety of known orfuture software programs for providing graphical input and outputinterfaces between the system and a user, and for processing userinputs. The functional elements of the computer may communicate witheach other via system bus. Some of these communications may beaccomplished in alternative embodiments using network or other types ofremote communications. The output manager may also provide informationgenerated by the processing module to a user at a remote location, e.g.,over the Internet, phone or satellite network, in accordance with knowntechniques. The presentation of data by the output manager may beimplemented in accordance with a variety of known techniques. As someexamples, data may include SQL, HTML or XML documents, email or otherfiles, or data in other forms. The data may include Internet URLaddresses so that a user may retrieve additional SQL, HTML, XML, orother documents or data from remote sources. The one or more platformspresent in the subject systems may be any type of known computerplatform or a type to be developed in the future, although theytypically will be of a class of computer commonly referred to asservers. However, they may also be a main-frame computer, a workstation, or other computer type. They may be connected via any known orfuture type of cabling or other communication system including wirelesssystems, either networked or otherwise. They may be co-located or theymay be physically separated. Various operating systems may be employedon any of the computer platforms, possibly depending on the type and/ormake of computer platform chosen. Appropriate operating systems includeWindows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux,OS/400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, and others.

FIG. 10 depicts a general architecture of an example computing device1000 according to certain embodiments. The general architecture of thecomputing device 1000 depicted in FIG. 10 includes an arrangement ofcomputer hardware and software components. It is not necessary, however,that all of these generally conventional elements be shown in order toprovide an enabling disclosure. As illustrated, the computing device1000 includes a processing unit 1010, a network interface 1020, acomputer readable medium drive 1030, an input/output device interface1040, a display 1050, and an input device 1060, all of which maycommunicate with one another by way of a communication bus. The networkinterface 1020 may provide connectivity to one or more networks orcomputing systems. The processing unit 1010 may thus receive informationand instructions from other computing systems or services via a network.The processing unit 1010 may also communicate to and from memory 1070and further provide output information for an optional display 1050 viathe input/output device interface 1040. For example, an analysissoftware (e.g., data analysis software or program such as FlowJo®)stored as executable instructions in the non-transitory memory of theanalysis system can display the flow cytometry event data to a user. Theinput/output device interface 1040 may also accept input from theoptional input device 1060, such as a keyboard, mouse, digital pen,microphone, touch screen, gesture recognition system, voice recognitionsystem, gamepad, accelerometer, gyroscope, or other input device.

The memory 1070 may contain computer program instructions (grouped asmodules or components in some embodiments) that the processing unit 1010executes in order to implement one or more embodiments. The memory 1070generally includes RAM, ROM and/or other persistent, auxiliary ornon-transitory computer-readable media. The memory 1070 may store anoperating system 1072 that provides computer program instructions foruse by the processing unit 1010 in the general administration andoperation of the computing device 1000. Data may be stored in datastorage device 1090. The memory 1070 may further include computerprogram instructions and other information for implementing aspects ofthe present disclosure.

Computer-Readable Storage Media

Aspects of the present disclosure further include non-transitorycomputer readable storage media having instructions for practicing thesubject methods. Computer readable storage media may be employed on oneor more computers for complete automation or partial automation of asystem for practicing methods described herein. In some embodiments,instructions in accordance with the method described herein can be codedonto a computer-readable medium in the form of “programming”, where theterm “computer readable medium” as used herein refers to anynon-transitory storage medium that participates in providinginstructions and data to a computer for execution and processing.Examples of suitable non-transitory storage media include a floppy disk,hard disk, optical disk, magneto-optical disk, CD-ROM, CD-ft magnetictape, non-volatile memory card, ROM, DVD-ROM, Blue-ray disk, solid statedisk, and network attached storage (NAS), whether or not such devicesare internal or external to the computer. In some instances,instructions may be provided on an integrated circuit device. Integratedcircuit devices of interest may include, in certain instances, areconfigurable field programmable gate array (FPGA), an applicationspecific integrated circuit (ASIC) or a complex programmable logicdevice (CPLD). A file containing information can be “stored” on computerreadable medium, where “storing” means recording information such thatit is accessible and retrievable at a later date by a computer. Thecomputer-implemented method described herein can be executed usingprogramming that can be written in one or more of any number of computerprogramming languages. Such languages include, for example, Java (SunMicrosystems, Inc., Santa Clara, Calif.), Visual Basic (Microsoft Corp.,Redmond, Wash.), and C++ (AT&T Corp., Bedminster, N.J.), as well as anymany others.

In some embodiments, computer readable storage media of interest includea computer program stored thereon, where the computer program whenloaded on the computer includes instructions for clustering fluorescentflow cytometer data into populations according to one or more differentparameters, determining the spillover spreading between eachdetector-parameter pair (i.e., by calculating spillover spreadingcoefficients), creating a spillover spreading matrix demonstrating howthe detection of a particular parameter by its corresponding detector isimpacted by spillover from other parameters, altering the fluorescentflow cytometer data to compensate for spillover spreading by subtractingthe magnitude of the spillover spreading as determined by the spilloverspreading matrix, evaluating the quality of different partitionsseparating distinct populations of fluorescent flow cytometer data bycalculating Matthew's correlation coefficient with respect to thresholdsdistinguishing between populations that are positive for a givenparameter and population that are negative for a given parameter, andclassifying (i.e., phenotyping) adjusted populations of fluorescent flowcytometer data.

In embodiments, the system is configured to analyze the data within asoftware or an analysis tool for analyzing flow cytometer data ornucleic acid sequence data, such as FlowJo®. The initial data can beanalyzed within the data analysis software or tool (e.g., FlowJo®) byappropriate means, such as manual gating, cluster analysis, or othercomputational techniques. The instant systems, or a portion thereof, canbe implemented as software components of a software for analyzing data,such as FlowJo®. In these embodiments, computer-controlled systemsaccording to the instant disclosure may function as a software “plugin”for an existing software package, such as FlowJo®.

The computer readable storage medium may be employed on more or morecomputer systems having a display and operator input device. Operatorinput devices may, for example, be a keyboard, mouse, or the like. Theprocessing module includes a processor which has access to a memoryhaving instructions stored thereon for performing the steps of thesubject methods. The processing module may include an operating system,a graphical user interface (GUI) controller, a system memory, memorystorage devices, and input-output controllers, cache memory, a databackup unit, and many other devices. The processor may be a commerciallyavailable processor, or it may be one of other processors that are orwill become available. The processor executes the operating system andthe operating system interfaces with firmware and hardware in awell-known manner, and facilitates the processor in coordinating andexecuting the functions of various computer programs that may be writtenin a variety of programming languages, such as Java, Perl, Python, C++,other high level or low level languages, as well as combinationsthereof, as is known in the art. The operating system also providesscheduling, input-output control, file and data management, memorymanagement, and communication control and related services, all inaccordance with known techniques.

Utility

The subject devices, methods and computer systems find use in a varietyof applications where it is desirable to increase resolution andaccuracy in the determination of parameters for analytes (e.g., cells,particles) in a biological sample. For example, the present disclosurefinds use in analyzing data that is affected by spillover spreading.Because flow cytometry often involves the collection of multiplefluorescent parameters by multiple detectors, detected fluorescent lightintensities may be erroneously increased due to the same light beingdetected by multiple detectors. As such, the present disclosure findsuse during the analysis of flow cytometer data that contains signalsfrom multiple fluorochromes. The subject devices, methods and computersystems also find use in classifying (i.e., phenotyping) populations offlow cytometer data that would normally be mischaracterized due to theeffects of spillover spreading. In some embodiments, the subject methodsand systems provide fully automated protocols so that adjustments todata require little, if any, human input.

The present disclosure can be employed to characterize many types ofanalytes, in particular, analytes relevant to medical diagnosis orprotocols for caring for a patient, including but not limited to:proteins (including both free proteins and proteins and proteins boundto the surface of a structure, such as a cell), nucleic acids, viralparticles, and the like. Further, samples can be from in vitro or invivo sources, and samples can be diagnostic samples.

Kits

Aspects of the present disclosure further include kits, where kitsinclude storage media such as a floppy disk, hard disk, optical disk,magneto-optical disk, CD-ROM, CD-ft magnetic tape, non-volatile memorycard, ROM, DVD-ROM, Blue-ray disk, solid state disk, and networkattached storage (NAS). Any of these program storage media, or othersnow in use or that may later be developed, may be included in thesubject kits. In embodiments, the program storage media includeinstructions for clustering fluorescent flow cytometer data intopopulations, determining the spillover spreading of the populations,adjusting flow cytometer data based on spillover spreading, as well asdetermining partitions between the adjusted flow cytometer data (e.g.,as described above). In embodiments, the instructions contained oncomputer readable media provided in the subject kits, or a portionthereof, can be implemented as software components of a software foranalyzing data, such as FlowJo®. In these embodiments,computer-controlled systems according to the instant disclosure mayfunction as a software “plugin” for an existing software package, suchas FlowJo®.

In addition to the above components, the subject kits may furtherinclude (in some embodiments) instructions, e.g., for installing theplugin to the existing software package such as FlowJo®. Theseinstructions may be present in the subject kits in a variety of forms,one or more of which may be present in the kit. One form in which theseinstructions may be present is as printed information on a suitablemedium or substrate, e.g., a piece or pieces of paper on which theinformation is printed, in the packaging of the kit, in a packageinsert, and the like. Yet another form of these instructions is acomputer readable medium, e.g., diskette, compact disk (CD), portableflash drive, and the like, on which the information has been recorded.Yet another form of these instructions that may be present is a websiteaddress which may be used via the internet to access the information ata removed site.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, it is readily apparent to those of ordinary skill in theart in light of the teachings of this invention that some changes andmodifications may be made thereto without departing from the spirit orscope of the appended claims.

Accordingly, the preceding merely illustrates the principles of theinvention. It will be appreciated that those skilled in the art will beable to devise various arrangements which, although not explicitlydescribed or shown herein, embody the principles of the invention andare included within its spirit and scope. Furthermore, all examples andconditional language recited herein are principally intended to aid thereader in understanding the principles of the invention and the conceptscontributed by the inventors to furthering the art, and are to beconstrued as being without limitation to such specifically recitedexamples and conditions. Moreover, all statements herein recitingprinciples, aspects, and embodiments of the invention as well asspecific examples thereof, are intended to encompass both structural andfunctional equivalents thereof. Additionally, it is intended that suchequivalents include both currently known equivalents and equivalentsdeveloped in the future, i.e., any elements developed that perform thesame function, regardless of structure. Moreover, nothing disclosedherein is intended to be dedicated to the public regardless of whethersuch disclosure is explicitly recited in the claims.

The scope of the present invention, therefore, is not intended to belimited to the exemplary embodiments shown and described herein. Rather,the scope and spirit of present invention is embodied by the appendedclaims. In the claims, 35 U.S.C. § 112(f) or 35 U.S.C. § 112(6) isexpressly defined as being invoked for a limitation in the claim onlywhen the exact phrase “means for” or the exact phrase “step for” isrecited at the beginning of such limitation in the claim; if such exactphrase is not used in a limitation in the claim, then 35 U.S.C. § 112(f) or 35 U.S.C. § 112(6) is not invoked.

1. A method of classifying fluorescent flow cytometer data, the methodcomprising: processing the flow cytometer data with a supervisedalgorithm configured to: cluster the fluorescent flow cytometer datainto populations; determine a measure of spillover spreading for thepopulations of fluorescent flow cytometer data; adjust the populationsof fluorescent flow cytometer data based on the determined spilloverspreading to generate distinct spillover spreading adjusted populations;and establish partitions between the distinct spillover spreadingadjusted populations of fluorescent flow cytometer data to classify thedistinct spillover spreading adjusted populations of fluorescent flowcytometer data.
 2. The method according to claim 1, wherein thefluorescent flow cytometer data is clustered into populations based onpositivity or negativity of the fluorescent flow cytometer data withrespect to particular fluorochromes.
 3. The method according to claim 1,wherein the fluorescent flow cytometer data is determined to be positiveor negative for a particular fluorochrome based on a relationship of thefluorescent flow cytometer data to a threshold value.
 4. The methodaccording to claim 1, wherein determining spillover spreading comprisesquantifying the extent to which fluorescent flow cytometer datacollected by a fluorescent light detector is increased by the collectionof light emitting from a particular fluorochrome.
 5. The methodaccording to claim 1, wherein determining spillover spreading comprisescalculating a spillover spreading coefficient for a fluorescent lightdetector-fluorochrome pair.
 6. The method according to claim 5, whereinthe spillover spreading coefficient is calculated according to Equation1:SS=(Δσ_ƒ)√Δd=√((σ_pos){circumflex over ( )}2-(σ_neg){circumflex over( )}2)/√(d_pos-d_neg) wherein: SS is the spillover spreadingcoefficient; Δσ_f is an incremental standard deviation indicating thespread of the emission between the positive and negative fluorescentflow cytometer data collected from a fluorochrome; and Δd is adifference in the intensity of the fluorescent light between thepositive and negative fluorescent flow cytometer data received by afluorescent light detector.
 7. The method according to claim 5, whereincalculating the spillover spreading coefficient comprises assuming thatthe intensity of fluorescent light collected by the fluorescent lightdetector for the negative population of flow cytometer data is zero. 8.The method according to claim 7, wherein the spillover spreadingcoefficient is calculated according to Equation 2:SS_=√(σ{circumflex over ( )}2−σ_0{circumflex over ( )}2)√d wherein: SSis the spillover spreading coefficient; σ{circumflex over ( )}2 is thestandard deviation of the positive population of fluorescent flowcytometer data; σ_0{circumflex over ( )}2 is an estimate of the standarddeviation of the negative population of fluorescent flow cytometer data;and d is the intensity of light collected by a fluorescent lightdetector.
 9. The method according to claim 8, wherein σ_0{circumflexover ( )}2 is calculated by linear regression.
 10. The method accordingto claim 1, wherein the fluorescent flow cytometer data is collectedfrom light emitting from a plurality of different fluorochromes.
 11. Themethod according to claim 10, wherein the plurality of differentfluorochromes ranges from 2 to 20 different fluorochromes. 12.(canceled)
 13. The method according to claim 10, wherein a spilloverspreading coefficient is calculated for each fluorescent lightdetector-fluorochrome pair.
 14. The method according to claim 10,wherein the spillover spreading coefficients calculated for eachfluorescent light detector-fluorochrome pair are combined in a spilloverspreading matrix.
 15. The method according to claim 14, wherein themethod further comprises computing the magnitude of spillover spreadingfor each of the plurality of different fluorochromes based on thespillover spreading matrix.
 16. The method according to claim 15,wherein adjusting fluorescent flow cytometer data comprises subtractingthe magnitude of the spillover spreading for each of the plurality ofdifferent fluorochromes from the populations of fluorescent flowcytometer data corresponding to that fluorochrome.
 17. The methodaccording to claim 1, wherein establishing partitions between distinctspillover spreading adjusted populations of fluorescent flow cytometerdata comprises evaluating the separation of the distinct spilloverspreading adjusted populations relative to a threshold value.
 18. Themethod according to claim 17, wherein evaluating the separation ofdistinct spillover spreading adjusted populations relative to athreshold value comprises calculating Matthew's correlation coefficient.19. The method according to claim 18, wherein the fluorescent flowcytometer data does not contain positive data for at least onefluorochrome.
 20. The method according to claim 19, wherein evaluatingthe separation of distinct spillover spreading adjusted populationsrelative to a threshold value comprises calculating balanced accuracy.21. The method according to claim 1, wherein the distinct spilloverspreading adjusted populations of fluorescent flow cytometer data arepartitioned according to a hierarchy. 22-66. (canceled)